What is the difference between ML observability and ML monitoring?

ML observability is a broader concept that deals with understanding how and why a model makes a particular prediction. In contrast, ML monitoring only identifies issues by measuring relevant model metrics to evaluate performance.

What are the essential elements of ML observability?

Model monitoring and explainability are two critical components of observability. Monitoring helps detect errors, and explainability helps understand the fundamental reason behind the error.

What are the challenges of ML observability?

The primary challenges include increasing model complexity, scalability, privacy concerns, explainability in multimodal domains, and differences in model deployment and explanation strategies.

Is ML observability applicable to all model types?

Yes. Experts can apply observability to simple and complex models to delve deeper into the model’s decision-making process.

What is model explainability?

Model explainability is interpreting a model’s behavior by understanding how it reaches a particular conclusion during inference.

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Contents

What is Model Observability?
Significance of Model Observability
Model Observability in Large Language Models (LLMs) and Computer Vision (CV)
Model Monitoring Techniques
Model Explainability Techniques
Model Observability with Encord Active
Challenges of Model Observability
Future Trends in Model Observability
Model Observability: Key Takeaways

Encord Blog

A Guide to Machine Learning Model Observability

January 19, 2024

7 mins

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Contents

What is Model Observability?
Significance of Model Observability
Model Observability in Large Language Models (LLMs) and Computer Vision (CV)
Model Monitoring Techniques
Model Explainability Techniques
Model Observability with Encord Active
Challenges of Model Observability
Future Trends in Model Observability
Model Observability: Key Takeaways

Written by

Haziqa Sajid

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Artificial intelligence (AI) is solving some of society's most critical issues. But, lack of transparency and visibility in building models make AI a black box. Users cannot understand what goes on behind the scenes when AI answers a question, gives a prediction, or makes a critical decision.

What happens when large language models (LLMs), like GPT-4 and LLaMA, make errors, but users fail to realize the mistake? Or how badly is the model’s credibility affected when users identify errors in the model outcome?

Customers lose trust, and the company behind the model can face serious legal and financial consequences. Remember how a minor factual error in Bard’s demo reportedly cost Google $100 billion in market value?

That’s where model observability comes into play. It helps understand how a model reaches an outcome using different techniques. In this article, you will:

Understand model observability and the importance
Challenges related to model observability
How model observability applies to modern AI domains, like computer vision and natural language processing (NLP)

What is Model Observability?

Model observability is a practice to validate and monitor machine learning (ML) model performance and behavior by measuring critical metrics, indicators, and processes to ensure that the model works as expected in production.

It involves end-to-end event logging and tracing to track and diagnose issues quickly during training, inference, and decision-making cycles. It lets you monitor and validate training data to check if it meets the required quality standards. It also assists in model profiling, detecting bias and anomalies that could affect the ML model's performance

Through observability, ML engineers can conduct root-cause analysis to understand the reason behind a particular issue. This practice allows for continuous performance improvement using a streamlined ML workflow that enables scalability and reduces time to resolution.

A significant component of observability is model explainability, which operates under explainable AI or XAI. XAI facilitates root-cause analysis by enabling you to investigate a model’s decision-making process. XAI optimizes model development, debugging, and testing cycles using tools and frameworks.

ML model observability is often used interchangeably with ML monitoring. However, an emphasis on model explainability and a focus on why a specific issue occurred makes ML observability broader than ML monitoring. While model monitoring only tells you where and what the problem is, observability goes further by helping you understand the primary reason behind a particular situation.

Learn more about the difference between model monitoring and model observability by reading our detailed article ML Observability vs. ML Monitoring

Significance of Model Observability

ML models in production can encounter several issues that can cause performance degradation and result in poor user experience. These issues can go unnoticed if model observability practices are not followed.

With a robust model observability pipeline, data scientists can prevent such problems and speed up the development lifecycle.

Below are a few factors that can go wrong during production and warrant the need for model observability.

Data Drift

Data drift occurs when the statistical properties of a machine learning model’s training data change. It can be a covariate shift where input feature distributions change or a model drift where the relationship between the input and target variables becomes invalid. The divergence between the real-world and training data distribution can occur for multiple reasons, such as changes in underlying customer behavior, changes in the external environment, demographic shifts, product upgrades, etc.

Data drift - Encord

Data Drift

Performance Degradation

As the machine learning application attracts more users, model performance can deteriorate over time due to model overfitting, outliers, adversarial attacks, and changing data patterns.

Data Quality

Ensuring consistent data quality during production is challenging as it relies heavily on data collection methods, pipelines, storage platforms, pre-processing techniques, etc. Problems such as missing data, labeling errors, disparate data sources, privacy restraints, inconsistent formatting, and lack of representativeness can severely damage data quality and cause significant prediction errors.

Let’s discuss how model observability helps.

Faster Detection and Resolution of Issues

Model observability solutions track various data and performance metrics in real-time to quickly notify teams if particular metrics breach thresholds and enhance model interpretability to fix the root cause of the problem.

Regulatory Compliance

Since observability tools maintain logs of model behavior, datasets, performance, predictions, etc., they help with compliance audits and ensure that the model development process aligns with regulatory guidelines. Moreover, they help detect bias by making the model decision-making process transparent through XAI.

Fostering Customer Trust

Model observability enables you to build unbiased models with consistent behavior and reduces major model failures. Customers begin to trust applications based on these models and become more willing to provide honest feedback, allowing for further optimization opportunities.

Now, let’s discuss how model observability improves the ML pipeline for different domains.

Model Observability in Large Language Models (LLMs) and Computer Vision (CV)

Standard machine learning observability involves model validation, monitoring, root-cause analysis, and improvement. During validation, an observability platform evaluates model performance on unseen test datasets and assesses whether a model suffers from bias or variance. It helps you detect issues during production through automated metrics. Finally, insights from the root-cause analysis help you improve the model and prevent similar problems from occurring again.

While the flow above applies to all ML models, monitoring and evaluation methods can differ according to model complexity.

For instance, LLMs and CV models process unstructured data and have complex inference procedures. So, model observability requires advanced explainability, evaluation, and monitoring techniques to ensure that the models perform as expected.

Let’s discuss the main challenges of such models and understand the critical components required to make model observability effective in these domains.

Model Observability in Large Language Models

LLMs can suffer from unique issues, as discussed below.

Hallucinations: Occurs when LLMs generate non-sensical or inaccurate responses to user prompts.
No single ground truth: A significant challenge in evaluating an LLM's response is the absence of a single ground truth. LLMs can generate multiple plausible answers, and assessing which is accurate is problematic.
Response quality: While responses may be factually accurate, they may not be relevant to a user’s prompt. Also, the language may be ambiguous with an inappropriate tone. The challenge here is to monitor response and prompt quality together, as a poorly crafted prompt will generate sub-optimal responses.
Jailbreaks: Specific prompts can cause LLMs to disregard security protocols and generate harmful, biased, and offensive responses.
Cost of retraining: Retraining LLMs is necessary to ensure that they generate relevant responses using the latest information. However, retraining is costly. It demands a robust infrastructure involving advanced hardware, personnel expenses, data management costs, etc.

You can mitigate the above challenges using a tailored model observability strategy that will allow for better evaluation techniques. Below are common techniques to evaluate LLMs.

User feedback: Users can quickly identify and report problems, such as bias, misinformation, and unethical LLM responses to specific prompts. Collecting and assessing user feedback can help improve response quality.
Embedding visualization: Comparing the model response and input prompt embeddings in a semantic space can reveal how close the responses to particular prompts are. The method can help evaluate response relevance and accuracy.

A word embedding plot showcasing relevance of similar words - Encord

A Word Embedding Plot Showcasing Relevance of Similar Words

Prompt engineering: You can enhance LLM performance by investigating how it responds to several prompt types. It can also help reveal jailbreaking prompts early in the development process.
Retrieval systems: A retrieval system can help you assess whether an LLM fetches the correct information from relevant sources. You can experiment by feeding different data sources into the model and trying various user queries to see if the LLM retrieves relevant content.
Fine-tuning: Instead of re-training the entire LLM from scratch, you can fine-tune the model on domain-specific input data.

Model Observability in Computer Vision

Similar to LLMs, CV models have specific issues that require adapting model observability techniques for effectiveness. Below are a few problems with CV modeling.

Image drift: Image data drift occurs when image properties change over time. For instance, certain images may have poor lighting, different background environments, different camera angles, etc.
Occlusion: Occlusion happens when another object blocks or hides an image's primary object of interest. It causes object detection models to classify objects wrongly and reduces model performance.

Occlusion - Encord

Occlusion

Lack of annotated samples: CV models often require labeled images for training. However, finding sufficient domain-specific images with correct labels is challenging. Labeling platforms like Encord can help you mitigate these issues.
Sensitive use cases: CV models usually operate in safety-critical applications like medical diagnosis and self-driving cars. Minor errors can lead to disastrous consequences.

As such, model observability in CV must have the following components to address the problems mentioned above.

Monitoring metrics: Use appropriate metrics to measure image quality and model performance.
Specialized workforce: Domain experts must be a part of the model observability pipeline to help annotators with the image labeling process.
Quality of edge devices: Image data collection through edge devices, such as remote cameras, drones, sensors, etc., requires real-time monitoring methods to track a device’s health, bandwidth, latency, etc.
Label quality: Ensuring high label quality is essential for effective model training. Automation in the labeling process with a regular review system can help achieve quality standards. Smart labeling techniques, such as active, zero-shot, and few-shot learning, should be part of the observability framework to mitigate annotation challenges.
Domain adaptation: CV observability should help indicate when fine-tuning a CV model is suitable by measuring the divergence between source and target data distribution. It should also help inform the appropriate adaptation technique.

Want to learn how active learning can solve your CV data annotation challenges? Read our detailed article A Practical Guide to Active Learning for Computer Vision.

Now, let’s explore the two crucial aspects of model observability: monitoring and explainability. We’ll discuss various techniques that you can employ in your ML pipelines.

Model Monitoring Techniques

The following model metrics are commonly used for evaluating the performance of standard ML systems, LLMs, and CV models.

Standard ML monitoring metrics: Precision, recall, F1 score, AUC ROC, and Mean Absolute Error (MAE) are common techniques for assessing model performance by evaluating true positives and negatives against false positives and negatives.
LLM monitoring metrics: Automated scores like BLEU, ROUGE, METEOR, and CiDER help measure the model response quality by matching n-grams in candidate and target texts. Moreover, human-level monitoring is preferable in generative AI domains due to the absence of a single ground truth. User feedback, custom metrics, and manually evaluating response relevance are a few ways to conduct human-based assessments. You can also implement reinforcement learning with human feedback (RLHF) to ensure LLM output aligns with human preferences.
CV monitoring metrics: Metrics for monitoring CV models include mean average precision (mAP), intersection-over-union (IoU), panoptic quality (PQ), etc., for various tasks, such as object detection, image classification, and segmentation.

Model Explainability Techniques

The primary difference between ML observability and monitoring is that the former encompasses a variety of explainability techniques (XAI) to help you understand the fundamental reason behind an anomaly or error.

XAI techniques can be classified into three levels of explainability:

Global,
Cohort,
Local.

Global explainability tells you which feature has the most significant contribution across all predictions on average. Global explainability helps people without a data science domain to interpret the model reasoning process.

For instance, the diagram below shows a model for predicting credit limits for individuals relies heavily on age. This behavior can be problematic since the model will always predict a lower credit limit for younger individuals, despite their ability to pay more.

Global Explainability for a model's features - Encord

Global Explainability for a Model’s Features

Similarly, cohort explainability techniques reveal which features are essential for predicting outputs on a validation or test dataset. A model may assign importance to one feature in the training phase but use another during validation. You can use cohort explainability to identify these differences and understand model behavior more deeply. The illustration below suggests the model uses “charges” as the primary feature for predicting credit limits for people under 30.

Cohort explainability - Encord

Finally, local explainability methods allow you to see which feature the model used for making a particular prediction in a specific context during production. For instance, the credit limit model may predict a lower credit limit for someone over 50. Analyzing this case may reveal that the model used account balance as the primary predictor instead of age. Such a level of detail can help diagnose an issue more efficiently and indicate the necessary improvements.

Explainability Techniques in Standard ML Systems

Let’s briefly discuss the two techniques you can use to interpret a model’s decision-making process.

SHAP: Shapley Additive Explanations (SHAP) computes the Shapley value of each feature, indicating feature importance for global and local explainability.
LIME: Local Interpretable Model-Agnostic Explanations (LIME) perturbs input data to generate fake predictions. It then trains a simpler model on the generated values to measure feature importance.

Explainability Techniques in LLMs

LLM explainability is challenging since the models have complex deep-learning architectures with many parameters. However, the explainability techniques mentioned below can be helpful.

Attention-based techniques: Modern LLMs, such as ChatGPT, BERT, T5, etc., use the transformer architecture, which relies on the attention mechanism to perform NLP tasks. The attention-based explainability technique considers multiple words and relates them to their context for predictions. With attention visualization, you can see which words in an input sequence the model considered the most for predictions.
Saliency-based techniques: Saliency methods consist of computing output gradients with respect to input features for measuring importance. The intuition is that features with high gradient values are essential for predictions. Similarly, you can erase or mask a particular input feature and analyze output variation. High variations mean that the feature is important.

Explainability Techniques in CV

While saliency-based methods are applicable for explaining CV model predictions, other methods specific to CV tasks can do a better job, such as:

Integrated gradients

The idea behind integrated gradients (IG) is to build a baseline image that contains no relevant information about an object. For instance, a baseline image can only have random noise. You can gradually add features, or pixels to the image and compute gradients at each stage. Adding up the gradients for each feature along the path will reveal the most important features for predicting the object within the image.

IG showing which pixels are important for predicting the image of a cockatoo - Encord

IG Showing Which Pixels Are Important For Predicting the Image of a Cockatoo

XRAI

XRAIenhances the IG approach by highlighting pixel regions instead of single pixels by segmenting similar image patches and then computing the saliency of each region through gradients.

XRAI showing the important regions for prediction - Encord

XRAI Showing the Important Regions for Prediction

Grad-CAM

Gradient-weighted Class Activation Mapping (Grad-CAM) generates a heatmap for models based on Convolutional Neural Nets (CNNs). The heatmap highlights the regions of importance by overlaying it on the original image.

Grad-CAM's Heatmap - Encord

Grad-CAM’s Heatmap

Model Observability with Encord Active

Encord Active is an evaluation platform for computer vision models. It helps you understand and improve your data, labels, and models at all stages of your computer vision journey. Here are some observability workflows you can use Encord Active to execute:

Understanding Model Failure Using Detailed Explainability Reports

Within Encord Active, you can visualize the impact of data and label quality metrics on your model's performance. This can also visualize Precision or Recall by Metric based on the model results to know which instances it needs to predict better. Let’s see how you can achieve this step-by-step within Encord Active.

Make sure you have a project selected and have successfully imported model predictions (through an API or the UI).

Step 1: Navigate to the Model Evaluation tab.
Step 2: Toggle the Metric Performance button. You will see a graph of your model’s performance based on the precision by the quality metric you select to identify model failure modes.

Model evaluation - Performance Metric - Encord

Debug Data Annotation Errors

When developing ML systems or running them in production, one key issue to look out for is the quality of your annotations. They can make or break the ML systems because they are only as good as the ground truth they are trained on. Here’s how you could do it within Active Cloud:

Step 1: Navigate to Explorer and Click Predictions
Step 2: Click Add Filter >> Annotation Quality Metrics, and select a metric you want to inspect.

Annotation Quality Metric - Encord

Perform Root-Cause Analysis

Encord Active provides a quick method to filter unwanted or problematic images by auto-discovering the issues in your model predictions. Here’s how to do it:

Ensure you are on the Explorer tab.
Click on Predictions and on the right hand side of your scream, you should see model prediction issues Active has surfaced including the types.

Root Cause Analysis - Encord

Those are shortcuts to improving datasets and model performance. They give you a quick launch pad to improve your data and model performance.

With Encord Active, you can also:

Use automated CV pipelines to build robust models.
Use pre-built or custom quality metrics to assess CV model performance.
Run robustness checks on your model before and after deployment.

Scale your annotation workflows and power your model performance with data-driven insights

Challenges of Model Observability

Despite effective tools, implementing an end-to-end model observability pipeline takes time and effort. The list below highlights the most pressing issues you must address before building an efficient observability workflow.

Increasing model complexity: Modern AI models are highly complex, with many integrated modules, deep neural nets, pre-processing layers, etc. Validating, monitoring, and performing root-cause analysis on these sophisticated architectures is tedious and time-consuming.
XAI in multimodal models: Explaining models that use multiple modalities - text, image, sound, etc. - is difficult. For instance, using salience-based methods to explain vision-language models that combine NLP and visual information for inferencing would be incredibly costly due to the sheer number of parameters.
Privacy concerns: Privacy restrictions mean you cannot freely analyze model performance on sensitive data, which may lead to compliance issues.
Scalability: Observing models that receive considerable inference requests requires a robust infrastructure to manage large data volumes.
Human understanding and bias: This challenge concerns XAI, where explanations can differ based on the model used. Also, understanding explainability results requires background knowledge that teams outside of data science may not have.

Future Trends in Model Observability

While the above challenges make optimal model observability difficult to achieve, research in the direction of XAI shows promising future trends.

For instance, there is considerable focus on making XAI more user-friendly by building techniques that generate simple explanations. Also, research into AI model fairness is ongoing, emphasizing using XAI to visualize learned features and employing other methods to detect bias.

In addition, studies that combine insights from other disciplines, such as psychology and philosophy, are attempting to find more human-centric explainability methods.

Finally, causal AI is a novel research area that aims to find techniques to highlight why a model uses a particular feature for predictions. This method can add value to explanations and increase model robustness.

Model Observability: Key Takeaways

As AI becomes more complex, observability will be crucial for optimizing model performance. Below are a few critical points you must remember about model observability.

Observability for optimality: Model observability is essential for optimizing modern AI models through efficient validation, monitoring, and root-cause analysis.
Better productivity: Observability improves resource allocations, reduces the cost of re-training, builds customer trust, ensures regulatory compliance, and speeds up error detection and resolution.
LLM and CV observability: Observability for LLMs and CV models differs slightly from standard ML observability due to issues specific to NLP and CV tasks.
Monitoring and explainability: Model monitoring and explainability are two primary components of observability, each with different techniques.
Challenges and future trends: Model complexity is the primary issue with observability and will require better explainability techniques to help users understand how modern models think.

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Haziqa Sajid

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Frequently asked questions

Model observability is the process of validating, monitoring, and finding the root cause of issues in machine learning models.
ML observability is a broader concept that deals with understanding how and why a model makes a particular prediction. In contrast, ML monitoring only identifies issues by measuring relevant model metrics to evaluate performance.
Model monitoring and explainability are two critical components of observability. Monitoring helps detect errors, and explainability helps understand the fundamental reason behind the error.
The primary challenges include increasing model complexity, scalability, privacy concerns, explainability in multimodal domains, and differences in model deployment and explanation strategies.
Yes. Experts can apply observability to simple and complex models to delve deeper into the model’s decision-making process.
Model explainability is interpreting a model’s behavior by understanding how it reaches a particular conclusion during inference.

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Top 15 DICOM Viewers for Medical Imaging

Digital Imaging and Communications in Medicine (DICOM) viewers are a global phenomenon as medical experts increasingly rely on these image-viewing solutions to analyze complex sequences such as CT scans, X-rays, MRIs, etc. DICOM viewers commonly integrate with the Picture Archiving and Communication System (PACS) to store, manage, share, and retrieve medical images. With PACS, DICOM viewers allow healthcare professionals to easily manipulate complex medical data for better diagnosis and patient care. Due to their increasing popularity, the projected compound annual growth rate (CAGR) for the Global Medical Imaging and Radiology software market stands at 7.8% from 2023 to 2030. This article discusses the top players in the market to help you find the best DICOM viewer for the job. How to Choose a DICOM Viewer? DICOM is an international standard introduced in 1993 that defines the format for storing and exchanging medical images. Using artificial intelligence (AI) in medical imaging to diagnose diseases is becoming the norm. This has led to many DICOM solutions with sophisticated annotation features and user interface (UI) enhancements entering the market. As such, the modern medical ecosystem needs a DICOM viewer to handle complex clinical trials requiring detailed analysis and collaboration among medical professionals. However, choosing an appropriate viewer is challenging due to the number of DICOM tools available. So, the list below narrows down the factors you should consider when purchasing a DICOM solution. Compatibility with Operating Systems: You should opt for a DICOM viewer compatible with your desired operating system. However, getting a viewer that works with Mac OS, Linux, and Windows is more practical, as solutions that work on a single platform can reduce collaboration efficiency across team members with different operating systems (or development environments). Ease of Setup: A DICOM viewer with a straightforward installation can be a significant time-saver. Consider a tool that could help your team get started quickly without additional configurations or dependencies. Patient Data Anonymization: With strict patient data regulations, a viewer must provide features allowing users to abstract sensitive information (PII) quickly to prevent data violations. Intuitive User Interface (UI): An easy-to-use UI simplifies a doctor’s job. Look for features that allow quick navigation and analysis of medical images, such as clear markers, scrolling capabilities, adjustable brightness, multiplanar reconstruction (MPR), and Maximum Intensity Projection (MIP). Select a DICOM viewer with your desired UI specifications for a better user experience. Reporting: A DICOM viewer capable of handling image fusion can facilitate more comprehensive and detailed reporting, allowing healthcare professionals to annotate and report findings directly on the fused images. Image fusion technology allows the combination of images from different imaging modalities, such as Positron Emission Tomography (PET), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). PACS Integration: Picture Archiving and Communication System (PACS) is the foundational software that helps with information exchange and storage between different medical devices. PACS can be local or cloud-based with improved accessibility. A DICOM viewer that can integrate quickly with a cloud-based PACS is a more appropriate option. Data Security Assurance and Compliance Certifications: The importance of privacy and security certifications like SOC2 cannot be overstated. It ensures that the solution maintains the highest data protection standards, vital in the healthcare sector. Cost-Effectiveness: Invest in a solution that balances price with the desired features at a reasonable cost for a faster return on investment (ROI). 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Encord DICOM Annotation Tool Features Compatibility with Operating Systems: Encord Annotate provides native support for DICOM within a web app compatible with all operating systems Ease of Setup: Setting up Encord is an easy 4-step process. Patient Data Anonymization: The de-identification function lets you protect sensitive patient data. Intuitive User Interface (UI): It has a user-friendly interface to render 20,000-pixel intensities, set custom window widths & levels, and natively display DICOM metadata. Reporting and PACS Integration: With Encord’s DICOM editor, you can annotate modalities from your Mammography, CT, X-ray, and MRI PACS viewer. Data Security Assurance and Compliance Certifications: The platform is SOC2 compliant and conforms to FDA regulations. Key Benefits Annotation Type: Label using any annotation type in 2D (with 3D in the works) and seamlessly toggle between axial, coronal, and sagittal views. Encord also supports several annotation techniques, such as polygons, hanging protocols, segmentation, etc. Full-range intensity values: Encord’s DICOM viewer natively renders files in the browser, supporting displays of up to 20,000 pixels. 2D Multiplanar Reconstruction (MPR): Encord lets you reconstruct images in 2D orthogonal planes through its MPR display for efficient annotation and visualization. Best for Healthcare professionals who want an end-to-end AI-based imaging tool. Pricing Encord has a simple pay-per-user pricing system. Learn more about the DICOM & NIfTI Annotation features Learn how to improve Machine Learning experiments with medical images by reading our Guide to Experiments for Medical Imaging in Machine Learning. 3DimViewer 3DimViewer is an open-sourceDICOM viewing multi-platform software that supports MicrosoftWindows, Mac OS X, and Linux. 3DIMViewer Features 3DimViewer is compatible with Linux, Mac OS, and Windows Installation involves downloading the application from the website. It features data anonymization. 3Dim’s user interface is a bit difficult to understand. Key Benefits 3DimViewer provides three-dimensional (3D) visualizations for DICOM images. Features volume rendering through shaders running on NVIDIA and AMD graphic cards. It offers adjustable-density windows to change brightness and contrast for viewing particular areas in medical scans more clearly. Best for Teams looking for a budget-friendly, lightweight, and versatile DICOM viewer. Pricing 3DIMViewer is a free DICOM viewer. PostDICOM PostDICOM is a cloud-based PACS with an integrated DICOM viewer that lets you view, save, and share DICOM files using PostDICOM’s cloud servers. PostDICOM Features PostDICOM is compatible with Linux, Windows, and Mac OS. PostDICOM can be a bit challenging to set up. It has a data anonymization feature. PostDICOM has a user-friendly interface. The solution is integratable with PACS. Key Benefits PostDICOM features an online DICOM viewer with advanced visualizations like MPR, MIP, and 3D rendering. It lets you view DICOM image files on any device. It helps you share data using a single link and password. Best for Teams that are looking for an end-to-end storage and viewing solution for large DICOM datasets. Pricing PostDICOM offers a Lite, Pro, and Advanced version. RadiAnt RadiAnt is a Windows-only local PACSDICOM viewer that supports dynamic sequences and monochromatic, color, and static images. RadiAnt User Interface Features RadiAnt is only compatible with Windows. Installation and setup require downloading the application and installing using the installing wizard. The user interface can be difficult to understand. It integrates with PACS. Key Benefits The RadiAnt Dicom Viewer features multiple user-friendly tools like fluid zooming and panning, a pen for freehand drawing, preset window settings, etc. It supports PET-CT image fusion and fusion with other modalities. It has multi-touch support for touch-enabled devices. Best for Teams that want a DICOM viewer with an intuitive UI for studying complex images for research purposes. Pricing RadiAnt has a subscription-based pricing mechanism. Horos Horos is an open-source DICOM viewer based on OsiriX, an image-processing library for the Apple Mac OS. Horos Features Horos is only compatible with Mac OS. The solution requires a 5-step setup procedure It integrates with PACS Key Benefits Horos lets you share studies with multiple recipients from within the platform. It integrates with the cloud for efficient storage and file transfer. Best for Teams that want a Mac-OS compatible viewer that can handle large image data. Pricing The tool is free to use. 3D Slicer 3D Slicer is an open-source DICOM software that lets you visualize, process, segment, and analyze 3D medical images. It also supports virtual and augmented reality (VR and AR). 3DSlicer Features 3D Slicer is compatible with Windows, Mac OS, and Linux. Installation is a bit challenging. It features data anonymization. The user interface is a bit complex. It supports PACS. Key Benefits 3D Slicer supports 2D, 3D, and 4D (VR/AR Supported) DICOM objects. It features over 150 plugins and extensions. The viewer is compatible with the latest AR/VR devices, like Oculus and HoloLens. Best for Medical scientists who want a real-time tool for navigating surgical workflows. Pricing The tool is open-source. Mango Multi-image Analysis GUI (Mango) is a lightweight open-source DICOM solution compatible with Windows, Mac OS, and Linux and offers plugins for Java and Python development. Mango Features Mango is compatible with Windows, Linux, and Mac OS. The user interface is complex. Key Benefits Mango supports multiple image formats, including DICOM, MINC, and NIFTI. It offers customizations for file formats, atlases, color tables, etc. It supports cut planes and overlays for interactive surface modeling. Best for Doctors who want to view and analyze DICOM and other image formats on the go. Pricing The software is free to use. Escape EMV Escape EMV is a Windows-based DICOM viewer with multilingual functionality and integrates with PACS servers to exchange DICOM files. Escape EMV Features Escape EMV is compatible with Windows and MacOS. The user interface is complex. Supports PACS. Key Benefits Escape EMV features a multilingual interface supporting English, French, Italian, Spanish, and Portuguese. It has a full-screen viewing mode. It lets you query servers to fetch your desired files quickly. Best for Teams that need a tool for collaborating across borders. Pricing Pricing is based on usage. Ginkgo CADx Ginkgo CADx is a multi-platform DICOM image viewer that can convert standard image files into the DICOM format. Ginkgo CADx Features Ginkgo CADx is compatible with Windows, Linux, and MacOSX. It has an easy-to-use interface Key Benefits Ginkgo CADx can convert PNG, JPEG, BMP, and TIFF formats to DICOM. It is compatible with Windows, Mac OS, and Linux. It supports Kitware’s Insight Segmentation and Registration Toolkit. Best for Healthcare professionals who frequently work with multiple image formats and want an easy-to-use DICOM viewer and converter. Pricing The tool is open-source. DICOM Web Viewer DICOM Web Viewer is a free online DICOM viewer based on Javascript and HTML 5, making it compatible with any platform that supports a modern browser. DICOM Web Viewer Features DICOM Web Viewer is compatible with all operating systems. It is easy to set up. Key Benefits DICOM Web Viewer can load data from local and remote servers. It has features like drag, contrast, and zoom to manipulate DICOM images. Best for Professionals who need a versatile viewer with basic functionality. Pricing The solution is free to use. Miele-LXIV Miele-LXIV is a 64-bit DICOM viewing application for Mac OS, compatible with versions 10.14 to 14.2. Miele-LXIV Features Miele-LXIV is only compatible with MAC OS Key Benefits Miele-LXIV supports MacOS Mojave’s Dark Mode. It supports seven languages: Chinese, English, French, German, Italian, Japanese, and Spanish. Its universal binary makes it run on Intel and Apple silicon processors efficiently. Best for Health professionals who work primarily on Mac OS to view DICOM images. Pricing It has a fixed price. Philips DICOM Viewer The Philips DICOM Viewer is a basic read-only application, which means you cannot save changes to an image. It only lets you export and view DICOM files. Philips DICOM Viewer Features The software is only compatible with Windows. Key Benefits The software features a series selector and viewer. It supports multiple image viewing types simultaneously for multimodal studies. Best for Physicians and technicians who want a tool for quickly viewing and printing DICOM images. Pricing Pricing is not publicly disclosed Weasis Weasis is a web-based DICOM solution with a modular architecture and integrations with PACS, Hospital Information Systems (HIS), Patient Health Records (PHR), and Radiology Information Systems (RIS). Weasis Features Weasis is compatible with Windows, Mac OS, and Linux. Installation requires direct download from the website. Supports data anonymization. It has a user-friendly interface. It integrates with PACS. Key Benefits Weasis lets you view multiple DICOM types, including MPEG-2, ECG, RT, etc. It offers several layouts to compare studies. It features tools for measuring and annotating images. Best for Healthcare practitioners who want an advanced solution for analyzing multiple clinical trials cheaply. Pricing Weasis is free to use. MiViewer MILLENSYS Dicom Viewer (MiViewer) is a general-purpose DICOM tool requiring no installation, making it easier to operate and view multimodality images. MiViewer Features MiViewer is easy to set up User interface is challenging to operate. Key Benefits The software lets you view cine loops with playback controls, special filters, and multi-frame images. It offers several tools to manipulate images, such as multi-format display, windowing, annotation, etc. It lets you export DICOM images as compressed JPEG files. Best for Practitioners who are new to the medical profession and want easy-to-use software. Pricing It is not publicly disclosed Yakami DICOM Yakami DICOM is a collection of free applications, such as a DICOM viewer, converter, table maker, PACS client, file mover, etc., to manage DICOM data. It is compatible with Windows versions 2000 to 10. Yakami DICOM Features Yakami is only compatible with Windows. It is difficult to set up as you have to configure multiple applications. Supports data anonymization. The user interface is challenging to operate. The software comes with a PACS client. Key Benefits The platform lets you magnify images by 1600% and supports window level and width adjustments for better clarity. The DICOM viewer supports multiple image formats, including JPEG, PNG, TIFF, W3C, etc. The Table Maker package automatically creates tables by reading headers in DICOM files. Best for Researchers who want free DICOM management tools to conduct independent studies. Pricing Yakami DICOM is free to use. DICOM Viewers for Medical Images: Key Takeaways After exploring various DICOM viewers, it's evident that there's a range of options with diverse functionalities for viewing, storing, and sharing medical images. Here are some essential takeaways to guide your choice: Functionality vs. Cost: While open-source DICOM viewers are an attractive option due to their low cost, paid solutions often offer enhanced customer support, regular software updates, and ongoing development to meet evolving medical imaging needs. However, some open-source tools also provide substantial functionality and active community support. Advanced visualization features: Essential features for a DICOM viewer include custom window presets for different imaging modalities, Multiplanar Reconstruction (MPR), Maximum Intensity Projection (MIP), and efficient tools for zooming, panning, marking, and annotating images. These capabilities are crucial for detailed medical image analysis. Support for PACS: DICOM viewer integration with Picture Archiving and Communication Systems (PACS) is vital in today's interconnected medical landscape. Such integration facilitates streamlined storage and exchange of extensive DICOM datasets, enhancing overall workflow efficiency and data management. In conclusion, when choosing a DICOM viewer, balance the considerations of cost and functionality with your specific needs for visualization features and system integration to find the most suitable solution for your medical imaging requirements.

Jan 18 2024

8 M

Data Operations

Best Image Annotation Tools for Computer Vision [Updated 2024]

Guide to the most popular image annotation tools that you need to know about in 2024. Compare the features and pricing, and choose the best image annotation tool for your use case. It’s 2024—annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we put together a list of the most popular image labeling tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool. A team of data scientists working on an autonomous driving project looking for large-scale labeling services. Or a data operations team working in healthcare looking for the right platform for your radiologists to accurately label CT scans. This guide will help you compare the top AI annotation tools and find the right one for you. We will compare each based on key factors - including image annotation service, support for different data types and use cases, QA/QC capabilities, security and data privacy, integration with the machine learning pipeline, and customer support. But first, let's explore the process of selecting an image annotation tool from the available providers. Choosing the right image annotation tool is a critical decision that can significantly impact the quality and efficiency of the annotation process. To make an informed choice, it's essential to consider several factors and evaluate the suitability of an image annotation tool for specific needs. Evaluating Image Annotation Tools for Computer Vision Projects Selecting the perfect image annotation tool is like choosing the perfect brush for your painting. Different projects require specific annotation needs that dictate how downstream components. When evaluating an annotation tool that fits your project specifications, there are a few key factors you have to consider. In this section, we will explore those key factors and practical considerations to help you navigate the selection process and find the most fitting AI annotation tool for your computer vision applications. Annotation Types: An effective labeling tool should support various annotation types, such as bounding boxes (ideal for object localization), polygons (useful for detailed object outlines), keypoints (for pose estimation), and semantic segmentation (for scene understanding). The tool must be adaptable to different annotation requirements, allowing users to annotate images with precision and specificity based on the task at hand. User Interface (UI) and User Experience (UX): The user interface plays a crucial role in the efficiency and accuracy of the annotation process. A good annotation tool should have an intuitive interface that is easy to navigate, reducing the learning curve for users. Clear instructions, user-friendly controls, and efficient workflows contribute to a smoother annotation experience. Scalability: Consider the tool's ability to scale with the growing volume of data. A tool that efficiently handles large datasets and multiple annotators is crucial for projects with evolving requirements. Automation and AI Integration: Look for image labeling tools that offer automation features, such as automatic annotation tools or features, to accelerate the annotation process. Integrating an AI photo editor into the annotation process can significantly refine the accuracy of annotations, especially in complex imaging scenarios, thereby enhancing both the speed and quality of data labeling. Integration with artificial intelligence (AI) algorithms can further enhance efficiency by automating repetitive tasks, reducing manual effort, and improving annotation accuracy. Collaboration and Workflow Management: Assess the data annotation tool's collaboration features, including version control, user roles, and workflow management. Collaboration tools are essential for teams working on complex annotation projects. Data Security and Privacy: Ensure that the tool adheres to data security and privacy standards like GDPR. Evaluate encryption methods, access controls, and policies regarding the handling of sensitive data. Pricing: Consider various pricing models, such as per-user, per-project, or subscription models. Also factor in scalability costs, and potential additional fees, ensuring transparency in the pricing structure. Once you've identified which factors are most important for you to evaluate image annotating tools, the next step is understanding how to assess their suitability for your specific use case. Most Popular Image Annotation Tools Let's compare the features offered by the best image annotation companies such as Encord, Scale AI, Label Studio, SuperAnnotate, CVAT, and Amazon SageMaker Ground Truth, and understand how they assist in annotating images. This article discusses the top 17 image annotation tools in 2024 to help you choose the right image annotation software for your use case. Encord Scale CVAT Label Studio Labelbox Playment Appen Dataloop SuperAnnotate V7 Labs Hive COCO Annotator Make Sense VGG Image Annotator LabelMe Amazon SageMaker Ground Truth VOTT Encord Encord is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management. Key Features Data Management: Compile your raw data into curated datasets, organize datasets into folders, and send datasets for labeling. AI-assisted Labeling: Automate 97% of your annotations with 99% accuracy using auto-annotation features powered by Meta's Segment Anything Model or GPT-4’s LLaVA. Collaboration: Integrate human-in-the-loop seamlessly with customized Workflows - create workflows with the no-code drag and drop builder to fit your data ops & ML pipelines. Quality Assurance: Robust annotator management & QA workflows to track annotator performance and increase label quality. Integrated Data Labeling Services for all Industries: outsource your labeling tasks to an expert workforce of vetted, trained and specialized annotators to help you scale. Video Labeling Tool: provides the same support for video annotation. One of the leading video annotation tools with positive customer reviews, providing automated video annotations without frame rate errors. Robust Security Functionality: label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Integrations: Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for Commercial teams: Teams translating from an in-house solution or open-source tool that require a scalable annotation workflow with a robust, secure, and collaborative enterprise-grade platform. Complex or unique use case: For teams that require advanced annotation tool and functionality. It includes, complex nested ontologies or rendering native DICOM formats. Pricing Simple per-user pricing – no need to track annotation hours, label consumption or data usage. Curious? Try it out Scale Scale AI, now Scale, is a data and labeling services platform that supports computer vision use cases but specializes in RLHF, user experience optimization, large language models, and synthetic data. Scale AI's Image Annotation Tool. Key Features Customizable Workflows: Offers customizable labeling workflows tailored to specific project requirements and use cases. Data labeling services: Provides high-quality data labeling services for various data types, including images, text, audio, and video. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Best for Teams Looking for a Labeling Tool: Scale is a very popular option for data labeling services. Teams Looking for Annotation Tools for Autonomous Vehicle Vision: Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases. Teams Looking for Medical Imaging Annotation Tools: Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their data annotators on the platform. Pricing On a per-image basis CVAT (Computer Vision Annotation Tool) CVAT is an open source image annotation tool that is a web-based annotation toolkit, built by Intel. For image labeling, CVAT supports four types of annotations: points, polygons, bounding boxes, and polylines, as well as a subset of computer vision tasks: image segmentation, object detection, and image classification. In 2022, CVAT’s data, content, and GitHub repository were migrated over to OpenCV, where CVAT continues to be open-source. Furthermore, CVAT can also be utilized to annotate QR codes within images, facilitating the integration of QR code recognition into computer vision pipelines and applications. CVAT Label Editor. Key Features Open-source: Easy and free to get started labeling images. Manual Annotation Tools: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and cuboids, catering to diverse annotation needs. Multi-platform Compatibility: Works on various operating systems such as Windows, Linux, and macOS, providing flexibility for users. Export Formats: CVAT offers support for various data formats including JSON, COCO, and XML-based like Pascal VOC, ensuring annotation compatibility with diverse tools and platforms. Best for Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset). Not preferable for commercial teams as it lacks scalability, collaborative features, and robust security. Pricing Free 💡 More insights on image labeling with CVAT: For a team looking for free image annotation tools, CVAT is one of the most popular open-source tools in the space, with over 1 million downloads since 2021. Other popular free image annotation alternatives to CVAT are 3D Slicer, Labelimg, VoTT (Visual Object Tagging Tool - developed by Microsoft), VIA (VGG Image Annotator), LabelMe, and Label Studio. If data security is a requirement for your annotation project… Commercial labeling tools will most likely be a better fit — key security features like audit trails, encryption, SSO, and generally-required vendor certifications (like SOC2, HIPAA, FDA, and GDPR) are usually not available in open-source tools. Further reading: Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision Label Studio Label Studio is another popular open source data labeling platform. It provides a versatile platform for annotating various data types, including images, text, audio, and video. Label Studio supports collaborative labeling, custom labeling interfaces, and integration with machine learning pipelines for data annotation tasks. Label Studio Image Annotation Tool. Key Features Customizable Labeling Interfaces: Flexible configuration for tailored annotation interfaces to specific tasks. Collaboration Tools: Real-time annotation and project sharing capabilities for seamless collaboration among annotators. Extensible: Easily connect to cloud object storage and label data there directly Export Formats: Label Studio supports multiple data formats including JSON, CSV, TSV, and VOC XML like Pascal VOC, facilitating integration and annotation from diverse sources for machine learning tasks. Best for Data scientists, machine learning engineers, and researchers or teams requiring versatile data labeling for images. Not suitable for teams with limited technical expertise or resources for managing an open source tool Price Free with enterprise plan available Labelbox Labelbox is a US-based data annotation platform founded in 2017. Like most of the other platforms mentioned in this guide, Labelbox offers both an image labeling platform, as well as labeling services. Labelbox Image Editor Key Features Data Management: QA workflows and data annotator performance tracking. Customizable Labeling Interface: 3rd party labeling services through Labelbox Boost. Automation: Integration with AI models for automatic data labeling to accelerate the annotation process. Annotation Type: Support for multiple data types beyond images, especially text. Best for Teams looking for a platform to quickly annotate documents and text. Teams carrying out annotation projects that are use-case specific. As generalist tools, platforms like Labelbox are great at handling a broad variety of data types. If you’re working on a unique use-case-specific annotation project (like scans in DICOM formats or high-resolution images that require pixel-perfect annotations), other commercial AI labeling tools will be a better fit: check out our blog exploring Best DICOM Labeling Tools. Pricing Varies based on the volume of data, percent of the total volume needing to be labeled, number of seats, number of projects, and percent of data used in model training. For larger commercial teams, this pricing may get expensive as your project scales. Playment Playment is a fully-managed data annotation platform. The workforce labeling company was acquired by Telus in 2021 and provides computer vision teams with training data for various use cases, supported by manual labelers and a machine learning platform. Playment Image Annotation Tool Key Features Data Labeling Services: Provides high-quality data labeling services for various data types including images, videos, text, and sensor data. Support: Global workforces of contractors and data labelers. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Audio Labeling Tool: Speech recognition training platform (handles all data types across 500+ languages and dialects). Best for Teams looking for a fully managed solution who do not need visibility into the process. Pricing Enterprise plan Appen Appen is a data labeling services platform founded in 1996, making it one of the first and oldest solutions in the market. The company offers data labeling services for a wide range of industries and in 2019, acquired Figure Eight to build out its software capabilities and help businesses also train and improve their computer vision models. Appen Image Annotation Tool Key Features Data Labeling Services: Support for multiple annotation types (bounding boxes, polygons, and image segmentation). Data Collection: Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation. Natural Language Processing: Supports natural language processing tasks such as sentiment analysis, entity recognition, and text classification. Image and Video Analysis: Analyzes images and videos for tasks such as object detection, image classification, and video segmentation. Best for Teams looking for image data sourcing and collection alongside annotation services. Pricing Enterprise plan Dataloop Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data Dataloop is an Israel-based data labeling platform that provides a comprehensive solution for data management and annotation projects. The tool offers data labeling capabilities across images, text, audio, and video annotation, helping businesses train and improve their machine learning models. Dataloop Image Annotation Tool Key Features Data Annotation: Features for image annotation tasks, including classification, detection, and semantic segmentation. Video Annotation Tool: Support for video annotations. Collaboration Tool: Features for real-time collaboration among annotators, project sharing, and version control for efficient teamwork. Data Management: Offers data management capabilities including data versioning, tracking, and organization for streamlined workflows. Best for Teams looking for a generalist annotation tool for various data annotation needs. Teams carrying out specific image and video annotation projects that are use-case specific. As generalist tools, platforms like Dataloop are built to support a wide variety of simple use cases, so other commercial platforms are a better fit if you’re trying to label use-case-specific annotation projects (like high-resolution images that require pixel-perfect annotations in satellite imaging or DICOM files for medical teams). Pricing Free trial and an enterprise plan. SuperAnnotate SuperAnnotate provides enterprise solutions for image and video annotation, catering primarily to the needs of the computer vision community. It provides powerful annotation tools and features tailored for machine learning and AI applications, offering efficient labeling solutions to enhance model training and accuracy. SuperAnnotate - Image Annotation Tool Key Features Multi-Data Type Support: Versatile annotation tool for image, video, text, and audio. AI Assistance: Integrates AI-assisted annotation to accelerate the annotation process and improve efficiency. Customization: Provides customizable annotation interfaces and workflows to tailor annotation tasks according to specific project requirements. Integration: Seamlessly integrates with machine learning pipelines and workflows for efficient model training and deployment. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Export Formats: SuperAnnotate supports multiple data formats, including popular ones like JSON, COCO, and Pascal VOC. Best for Larger teams working on various machine learning solutions looking for a versatile annotation tool. Pricing Free for early stage startups and academics for team size up to 3. Enterprise plan V7 Labs V7 is a UK-based data annotation platform founded in 2018. The company enables teams to annotate training data, support the human-in-the-loop processes, and also connect with annotation services. V7 offers annotation of a wide range of data types alongside image annotation tooling, including documents and videos. V7 Labs Image Annotation Tool Key Features Collaboration Capabilities: Project management and automation workflow functionality, with real-time collaboration and tagging. Data Labeling Services: Provides labeling services for images and videos. AI Assistance: Model-assisted annotation of multiple annotation types (segmentation, detection, and more). Best for Students or teams looking for a generalist platform to easily annotate different data types in one place (like documents, images, and short videos). Limited functionalities for use-case specific annotations. Pricing Various options, including academic, business, and pro. Hive Hive was founded in 2013 and provides cloud-based AI solutions for companies wanting to label content across a wide range of data types, including images, video, audio, text, and more. Hive Image Annotation Tool Key Features Image Annotation Tool: Offers annotation tools and workflows for labeling images along with support for unique image annotation use cases (ad targeting, semi-automated logo detection). Ease of Access: Flexible access to model predictions with a single API call. Integration: Seamlessly integrates with machine learning pipelines and workflows for AI model training and deployment. Best for Teams labeling images and other data types for the purpose of content moderation. Pricing Enterprise plan COCO Annotator COCO Annotator is a web-based image annotation tool, crafted by Justin Brooks under the MIT license. Specifically designed to streamline the process of labeling images for object detection, localization, and keypoints detection models, this tool offers a range of features that cater to the diverse needs of machine learning practitioners and researchers. COCO Annotator - Image Annotation Tool Key Features Image Annotation: Supports annotation of images for object detection, instance segmentation, keypoint detection, and captioning tasks. Export Formats: To facilitate large-scale object detection, the tool exports and stores annotations in the COCO format. Automations: The tool makes annotating an image easier by incorporating semi-trained models. Additionally, it provides access to advanced selection tools, including the MaskRCNN, Magic Wand and DEXTR. Best For ML Research Teams: COCO Annotator is a good choice for ML researchers, preferable for image annotation for tasks like object detection and keypoints detection. Price Free Make Sense Make Sense AI is a user-friendly and open-source annotation tool, available under the GPLv3 license. Accessible through a web browser without the need for advanced installations, this tool simplifies the annotation process for various image types. Make Sense - Image Annotation Tool Key Features Open Sourced: Make Sense AI stands out as an open-source tool, freely available under the GPLv3 license, fostering collaboration and community engagement for its ongoing development. Accessibility: It ensures web-based accessibility, operating seamlessly in a web browser without complex installations, promoting ease of use across various devices. Export Formats: It facilitates exporting annotations in multiple formats (YOLO, VOC XML like Pascal VOC, VGG JSON, and CSV), ensuring compatibility with diverse machine learning algorithms and seamless integration into various workflows. Best For Small teams seeking an efficient solution to annotate an image. Price Free VGG Image Annotator VGG Image Annotator (VIA) is a versatile open-source tool crafted by the Visual Geometry Group (VGG) for the manual annotation of both image and video data. Released under the permissive BSD-2 clause license, VIA serves the needs of both academic and commercial users, offering a lightweight and accessible solution for annotation tasks. VGG Image Annotator - Image Annotation Tool Key Features Lightweight and User-Friendly: VIA is a lightweight, self-contained annotation tool, utilizing HTML, Javascript, and CSS without external libraries, enabling offline usage in modern web browsers without setup or installation. Offline Capability: The tool is designed to be used offline, providing a full application experience within a single HTML file of size less than 200 KB. Multi-User Collaboration: Facilitates collaboration among multiple annotators with features such as project sharing, real-time annotation, and version control. Best For VGG Image Annotator (VIA) is ideal for individuals and small teams involved in projects for academic researchers. Price Free LabelMe LabelMe is an open-source web-based tool developed by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) that allows users to label and annotate images for computer vision research. It provides a user-friendly interface for drawing bounding boxes, polygons, and semantic segmentation masks to label objects within images. LabelMe Image Annotation Tool Key Features Web-Based: Accessible through a web-based interface, allowing for annotation tasks to be performed in any modern web browser without requiring software installation. Customizable Interface: Provides a customizable annotation interface with options to adjust settings, colors, and layout preferences to suit specific project requirements. Best for Academic and research purposes Pricing Free Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth is a fully managed data labeling service provided by Amazon Web Services (AWS). It offers a platform for efficiently labeling large datasets to train machine learning models. Ground Truth supports various annotation tasks, including image classification, object detection, semantic segmentation, and more. Amazon SageMaker Ground Truth - Image Annotation Tool Key Features Managed Service: Fully managed by AWS, eliminating the need for infrastructure setup and management. Human-in-the-Loop Labeling: Harnesses the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. Scalability: Capable of handling large-scale annotation projects and accommodating growing datasets and annotation needs. Integration with Amazon SageMaker: Seamlessly integrates with Amazon SageMaker for model training and deployment, providing a streamlined end-to-end machine learning workflow. Best for Teams requiring large-scale data labeling. Pricing Varies based on labeling task and type of data. VOTT VOTT or Visual Object Tagging Tool is an open-source tool developed by Microsoft for annotating images and videos to create training datasets for computer vision models. VOTT provides an intuitive interface for drawing bounding boxes around objects of interest and labeling them with corresponding class names. VOTT Image Annotation Tool Key Features Versatile Annotation Tool: Supports a wide range of annotation types including bounding boxes, polygons, polylines, points, and segmentation masks for precise labeling. Video Annotation: Enables annotation of videos frame by frame, with support for object tracking and interpolation to streamline the annotation process. Multi-Platform Compatibility: Works across various operating systems such as Windows, Linux, and macOS, ensuring flexibility for users. Best for Teams requiring lightweight and customizable annotation tool for object detection. Pricing Free Image Annotation Tool: Key Takeaways There you have it! The 17 Best Image Annotation Tools for computer vision in 2024. For further reading, you might also want to check out a few 2024 honorable mentions, both paid and free annotation tools: Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. Labelimg - Labelimg is an open source multi-modal data annotation tool now part of Label Studio. MarkUp - MarkUp image is a free web annotation tool to annotate an image or a PDF.

Mar 26 2024

10 M

Machine Learning

Model Drift: Best Practices to Improve ML Model Performance

Machine learning (ML) models can learn, adapt, and improve. But, they are not immune to “wear and tear” like traditional software systems. A recent MIT and Harvard study states that 91% of ML models degrade over time, deduced from experiments conducted on 128 model and dataset pairs. Production-deployed machine learning models can degrade significantly over time due to data changes or become unreliable due to sudden shifts in real-world data and scenarios. This phenomenon is known as 'model drift,' and it can present a severe challenge for ML practitioners and businesses. In this article, we will: Discuss the concept of model drift in detail Explore the factors that cause model drift and present real-world examples Talk about the role of model monitoring in detecting model drift Learn the best practices for avoiding model drift and improving your ML models' overall performance By the end, readers will gain an intuitive understanding of model drift and ways to detect it. It will allow ML practitioners to build robust, long-serving machine learning models for high-profile business use cases. What is Model Drift in Machine Learning? Model drift is when a machine learning model's ability to predict the future worsens over time because of changes in the real-world data distribution, which is the range of all possible data values or intervals. These changes can be caused by the statistical properties of the input features or target variables. Simply put, the model’s predictions fail to adapt to evolving conditions. For instance, during the COVID-19 pandemic, consumer behavior was significantly shifted as people started working from home, leading to a surge in demand for home office and home gym equipment. In such circumstances, machine learning models cannot account for sudden shifts in consumer behavior and experience significant model drift. They would fail to accurately forecast demand, leading to inventory imbalances and potential losses. A Model Ageing Chart Based on a Model Degradation Experiment The chart above illustrates an experiment where the model error variability increases over time. The model shows good quality or low errors right after training, but the errors continue to increase with every passing time interval. Hence, detecting and mitigating model drift is crucial for maintaining the reliability and performance of the models in real-world environments. Types of Model Drift in Machine Learning There are three major types of model drift in machine learning, including: Data Drift Also known as covariate shift, this type of drift occurs when the source data distribution differs from the target data distribution. It is common with production-deployed ML models. Most of the time, the data distribution during model training differs from the data distribution at production because: The training data is not representative of the source population. The training data population differs from the target data population. Read our detailed article on How to Detect Data Drift on Datasets. Concept Drift This happens when the relationship between the input features and the target variable changes over time. Concept drift does not necessarily depict a change in the input data distribution but rather a drift in the relationship between the dependent and independent variables. It can be due to various hidden, inexplicable factors, for example, seasonality in weather data. Concept drift can be sudden, gradual, recurring, or incremental. Types of Concept Drift in Machine Learning Label Drift This happens when the distribution of the target variable changes over time due to reasons like labeling errors or changes in labeling criteria. Learn how to find and fix label errors in this article by Nikolaj Buhl. What Is the Cause of Model Drift? Understanding the root cause of machine learning model drift is vital. Several factors contribute to model drift in ML models, such as: Changes in the distribution of real-world data: When the distribution of the present data changes compared to the models’ training data. Since the models' training data contains outdated patterns, the model may not perform well on new data, reducing model accuracy. Changes in consumer behavior: When the target audience's behavior changes over time, the model's predictions become less relevant. This can occur due to changes in trends, political decisions, climate change, media hype, etc. For example, if a recommendation system is not updated to account for frequently changing user preferences, the system will generate stale recommendations. Model architecture: If a machine learning model is too complex, i.e., has many layers or connections, it can overfit small and unrepresentative training data and perform poorly on real-world data. On the other hand, if the model is too simple, i.e., it has few layers, it can underfit the training data and fail to capture the underlying patterns. Data quality issues: Using inaccurate, incomplete, or noisy training data can adversely affect an ML model’s performance. Adversarial attacks: Model drift can occur when the model is attacked by malicious actors to manipulate the input data, causing it to make incorrect predictions. Model and code changes: New updates or modifications to the model’s codebase can introduce drift if not properly managed. 🔎 Learn how to detect data quality issues in Torchvision Dataset with Encord Active in our comprehensive walkthrough. Prominent Real-World Examples of Model Drift Model drift deteriorates ML systems deployed across various industries. Model Drift in Healthcare Medical machine learning systems are mission-critical, making model drift significantly dangerous. For example, consider an ML system trained for cervical cancer screening. If a new screening method, such as the HPV test, is introduced and integrated into clinical practice, the model has to be updated accordingly, or its performance will deteriorate. In Berkman Sahiner and Weijie Chen's paper titled Data Drift in Medical Machine Learning: Implications and Potential Remedies, they show that data drift can be a major cause of performance deterioration in medical ML models. They show that in the event of a concept shift leading to data drift, a full overhaul of the model is required because a discrete change, however small, may not produce the desired results. Model Drift in Finance Banks use credit risk assessment models when giving out loans to their customers. Sometimes, changes in economic conditions, employment rates, or regulations can affect a customer’s ability to repay loans. If the model is not regularly updated with real-world data, it may fail to identify higher-risk customers, leading to increased loan defaults. Also, many financial institutions rely on ML models to make high-frequency trading decisions, and it is very common for market dynamics to change in split seconds. Model Drift in Retail Today, most retail companies rely on ML models and recommender systems. In fact, the global recommendation engine market is expected to grow from $5.17B in 2023 to $21.57B in 2028. Companies use these systems to determine product prices and suggest products to customers based on their past purchase history, browsing behavior, seasonality patterns, and other factors. However, if there is a sudden change in consumer behavior, the model may fail to forecast demand accurately. For instance, a retail store wants to use an ML model to predict its customers' purchasing behavior for a new product. They have trained and tested a model thoroughly on historical customer data. But before the product launch, a competitor introduces a similar product, or the market dynamics are disrupted due to external factors, like global inflation. As a result, these events could significantly influence how consumers shop and, in turn, make the ML model unreliable. Model Drift in Sales and Marketing Sales and marketing teams tailor their campaigns and strategies using customer segmentation models. If customer behavior shifts or preferences change, the existing segmentation model may no longer accurately represent the customer base. This can lead to ineffective marketing campaigns and reduced sales. Drawbacks of Model Drift in Machine Learning Model drift affects various aspects of model performance and deployment. Besides decreased accuracy, it leads to poor customer experience, compliance risks, technical debt, and flawed decision-making. Model Performance Degradation When model drift occurs due to changes in consumer behavior, data distribution, and environmental changes, the model's predictions become less accurate, leading to adverse consequences, especially in critical applications such as healthcare, finance, and self-driving cars. Poor User Experience Inaccurate predictions can lead to a terrible user experience and cause users to lose trust in the application. This may, in turn, damage the organization's or product's reputation and lead to a loss of customers or business opportunities. Technical Debt Model drift introduces technical debt, which accumulates unaddressed issues that make it harder to maintain and improve the model over time. If model drift occurs frequently, data scientists and ML engineers may need to repeatedly retrain or modify the model to maintain its performance, adding to the overall cost and complexity of the system. How to Detect Model Drift? The Role of Model Monitoring in Machine Learning A Generalized Workflow of Model Drift Detection in Machine Learning What is Model Monitoring in Machine Learning? Model monitoring is continuously observing and evaluating the performance of machine learning models in the production environment. It plays a crucial role in ensuring that ML models remain accurate and reliable over time, especially in the face of model drift. It involves logging and tracking the input data, model predictions, metadata, and performance metrics to identify changes affecting the model's accuracy and reliability. Model monitoring is important for the following reasons: Identifying drift: Model monitoring helps to identify model drift in production. It enables you to be in control of your product, detect issues early on, and immediately intervene when action is needed. A robust monitoring system can notify data science teams when the data pipeline breaks, a specific feature is unavailable in production data, or the model becomes stale and must be retrained. Improving accuracy: By closely monitoring how the model performs in the production environment, you have insights to remedy various issues, such as model bias, labeling errors, or inefficient data pipelines. Taking corrective actions (such as model retraining or swapping models) upon detecting drift makes each version of your model more precise than the previous version, thus delivering improved business outcomes. Creating responsible AI: An important part of building responsible AI is understanding how and why it does what it does. Model monitoring helps with AI explainability by providing insights into the model's decision-making processes and identifying undesirable model behavior. Detecting outliers: Model monitoring tools provide automated alerts to help ML teams detect outliers in real time, enabling them to proactively respond before any harm occurs. Want to learn more about ML model monitoring? Read our detailed article on ML Monitoring vs. ML Observability. How to Detect Model Drift in Machine Learning? Model drift can be detected using these two model drift detection techniques: Statistical Tests Statistical tests primarily involve comparing the predicted values from the ML model to the actual values to understand the performance. They usually vary in how the tests are carried out; let’s discuss a few below: Kolmogorov-Smirnov Test (KS Test): The Kolmogorov-Smirnov test compares the cumulative distribution functions (CDFs) of two samples to determine if they are statistically different. It is a non-parametric test that does not assume any specific distribution for the data. Kullback-Leibler (KL) Divergence: KL Divergence measures the difference between the model's predicted and actual data distribution. Its sensitivity to outliers and zero probabilities is an important characteristic you should be aware of. In practice, it means that when applying KL Divergence, carefully consider the nature of your data and distributions being compared. Jensen-Shannon Divergence (JSD): Jensen-Shannon Divergence measures the similarity between two probability distributions. It is suitable in cases where the underlying data distributions are not easily compared using traditional statistical tests, such as when the distributions are multimodal, complex, or have different shapes. It is less sensitive to outliers than KL Divergence, making it a robust choice for noisy data. Chi-Square: The Chi-square is a non-parametric test that measures the categorical data for a model over time. It is commonly used to test for independence between two categorical variables, their goodness-of-fit and homogeneity. Population Stability Index: PSI is a statistical measure that quantifies the difference between the distribution of two data ranges. It is a symmetric metric that monitors a variable against a baseline value during machine learning deployments. A value close to zero denotes no or minimal deviation, and a larger value denotes a possible drift in the data trend. Other statistical tests include the Populations Stability Index, the K-Sample Anderson-Darling Test, and the Wasserstein distance. Concept Drift Detection Framework ML Monitoring Tools Monitoring tools enable teams to debug models, find and fix data errors, and run automated tests to analyze model errors. They can deliver detailed explainability reports to teams for quick mitigation. Encord Active is a prominent example of a machine learning monitoring tool. Monitoring Model Drift with Encord Active (EA) EA is a robust evaluation and monitoring platform that can measure numerous model performance metrics to identify significant data shifts or model deviations. It provides a real-time chart to monitor the distribution of your image metrics or properties over the entire training set and also model quality evaluation metrics. The following steps will show you how to monitor your model’s predictions when you import a project from Annotate to Active Cloud. Learn how to import a project in this quick-start guide. Step 1: Choose a Project Projects within Encord represent the combination of an ontology, dataset(s), and team member(s) to produce a set of labels. Choose the project containing the datasets you imported from Annotate. This example uses the COCO 2017 dataset imported from Annotate: Step 2: Import your Model Predictions to Active Cloud Active provides metrics and analytics to optimize your model's performance. You must import predictions to Active before viewing metrics and analytics on your model performance. You can import them programmatically using an API (see the docs) or the user interface (UI). An ideal setup here may be to use the Predictions API to import the predictions from a production model into Active. In this walkthrough, we imported a set of predictions from a MaskRCNN model trained and evaluated on the COCO 2017 dataset. Step 3: Explore your Model's Performance Under the Model Evaluation Tab Navigate to the Model Evaluation tab to see the model performance metrics: Click on Model Performance to see a dashboard containing the performance metrics peculiar to your model: Visualize the important performance metrics (Average-Precision (mAP), Average-Recall (mAR), F1, True Positive Count, False Positive Count, False Negative Count) for your model. You can also visualize your model’s performance metrics based on different classes and intersection-over-union (IoU) thresholds. Active supports performance metrics for bounding boxes (object detection), polygons (segmentation), polylines, bitmasks, and key points. Best Practices for Improving Model Performance and Reducing Model Drift Choosing the Correct Metrics Different ML models require different evaluation and monitoring metrics to capture their performance accurately. There must be a suitable baseline performance to compare metric values. The chosen metrics must align with the specific goals and characteristics of the model. For instance, Classification models are better evaluated using accuracy, precision, and recall metrics. Regression models are assessed using MAE, MSE, RMSE, or R-squared. Natural language processing models can leverage BLEU, ROUGE, and perplexity metrics. When choosing your metric, it is important to understand its underlying principles and limitations. For example, a model trained on imbalanced data may exhibit high accuracy values but fail to account for less frequent events. In such cases, metrics like the F1 score or the AUC-ROC curve would provide a more comprehensive assessment. Want to learn more about classification metrics? Read our detailed article on Accuracy vs. Precision vs. Recall in Machine Learning: What is the Difference? Metric Monitoring Continuously tracking the model's performance metrics, such as accuracy, precision, recall, F1 score, and AUC-ROC curve, can help identify any deviations from its expected performance. This can be done using automated monitoring tools or manually reviewing model predictions and performance reports. Analyzing Data Distribution Constantly monitor the data distribution of input features and target variables over time to detect any shifts or changes that may indicate model drift. This can be done using statistical tests, anomaly detection algorithms, or data visualization techniques. Setting Up Data Quality Checks Accurate data is necessary for good and reliable model performance. Implementing robust data quality checks helps identify and address errors, inconsistencies, or missing values in the data that could impact the model's training and predictions. You can use data visualization techniques and interactive dashboards to track these data quality changes over time. Leverage Automated Monitoring Tools Automated monitoring tools provide an easier way to track model performance and data quality across the ML lifecycle. They provide real-time alerts, historical tracking with observability, and logging features to facilitate proactive monitoring and intervention. Automated tools also reduce the team’s overhead costs. Continuous Retraining and Model Versioning Periodically retraining models with updated data helps teams adapt to changes in data distribution and maintain optimal performance. Retraining can be done manually or automated using MLOps deployment techniques, which include continuous monitoring, continuous training, and continuous deployment as soon as training is complete. Model versioning allows for tracking the performance of different model versions and comparing their effectiveness. This enables data scientists to identify the best-performing model and revert to previous versions if necessary. Human-in-the-Loop Monitoring (HITL) It is very important to have an iterative review and feedback process conducted by expert human annotators and data scientists (HITL), as it can: Help validate the detected model drifts and decide whether they are significant or temporary anomalies. Help decide when and how to retrain the model when model drift is detected. Model Drift: Key Takeaways Model drift in machine learning can create significant challenges for production-deployed ML systems. However, applying the above mentioned best practices can help mitigate and reduce model drift. Some key points you must remember include: Model drift occurs due to changes in training or production data distribution. It can also occur due to extensive changes in the system code or architecture. Adversarial attacks on ML models can introduce data drift and result in performance degradation. Model drift can severely affect critical domains like healthcare, finance, and autonomous driving. You can detect model drift by applying statistical tests or automated model ML monitoring tools. You must select the right set of metrics to evaluate and monitor an ML system. You must set up data quality checks to minimize data drift across training and production workflows. Retrain the model after specific intervals to keep it up-to-date with changing environments.

Jan 04 2024

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