In machine learning, training data plays a vital role in the accuracy and effectiveness of models. However, not all data is created equal, and the presence of outliers can significantly impact the performance of these models.  In this blog post, we will explore the concept of outlier detection and how it can be leveraged to improve training data with Encord Active. What is Outlier Detection? Outlier detection refers to identifying data points that deviate significantly from the normal distribution of a dataset. Outliers can arise due to various factors such as measurement errors, data corruption, or anomalies in the data. Detecting and handling outliers is crucial as they can distort statistical analysis and affect the performance of your ML models. {{Training_data_CTA}} In data analysis and machine learning, you can encounter two types of outliers:  Data outliers  Label outliers Data Outliers Data outliers refer to observations or instances in a dataset that significantly deviate from the expected or typical values. These outliers arise due to measurement errors, data corruption, or anomalies.  Data outliers can distort statistical analysis, affect the performance of machine learning models, and lead to inaccurate predictions. Detecting and addressing data outliers is crucial to ensure a high-quality dataset. Label Outliers Label outliers pertain to mislabeled or incorrectly assigned labels in a dataset. These outliers can occur due to human error during the labeling process or ambiguous instances that are challenging to classify accurately.  Label outliers can substantially impact the performance of supervised learning algorithms by introducing noise and misguiding the training process. Identifying and rectifying label outliers is essential for training models with accurate ground truth and improving their predictive capabilities. Both data outliers and label outliers require careful analysis and handling to ensure the quality and reliability of data for your machine learning tasks. You must employ robust outlier detection techniques and quality assurance procedures to identify and address these outliers for more accurate and dependable models. Outlier Detection in Encord Active Encord Active offers a robust solution to identify and label outliers for pre-defined metrics, custom metrics, and label classes using precomputed interquartile ranges  With this feature, you can easily spot data points that deviate significantly from the norm, enabling you to take appropriate actions and ensure data quality. By leveraging Interquartile ranges, Encord Active streamlines your outlier detection workflow, helping to debug your data. Setup To install Encord Active, follow these simple commands in your favorite Python environment: python3.9 -m venv ea-venv source ea-venv/bin/activate pip install encord-active {{gray_callout_start}} 💡 Check out the documentation for installation for more information. {{gray_callout_end}} Let's explore the steps in improving training data with outlier detection in Encord Active. Here we will be using the BDD dataset. {{gray_callout_start}} 💡 Check out the documentation for downloading the BDD dataset in Encord Active. {{gray_callout_end}} Data Outliers Finding the Data Outliers Encord Active provides an intuitive interface to locate outliers in your dataset. Navigate to the Data Quality > Summary tab and access the Quality Metrics, presented as expandable panes.  Click on a specific metric to reveal moderate to severe outliers, with the most severe ones displayed first, and use the slider to navigate through Tagging the Data Outliers Once you identify outliers of interest, Encord Active allows you to tag outliers individually or in bulk so you can easily manage and work with them for further analysis. Acting on the Data Outliers After creating the tagged image group, access it conveniently in the Actions tab at the bottom of the left sidebar with a range of actions at your disposal.  Select "Filter data frame on" and choose the "tags" option, to focus on the tagged outliers. You can then export the outliers, relabel them, augment the data, review them in detail, or even delete them from your dataset. Label Outliers Find Label Outliers To begin, navigate to the Label Quality > Summary tab in Encord Active. Here, you will find each Quality Metric presented as expandable panes, providing an overview of label quality. Click on a specific metric to gain deeper insights into moderate and severe label outliers. Like data outliers, the pane will prioritize presenting the severe outliers first, allowing you to focus on the most critical issues. Tag Label Outliers Once you have identified label outliers of interest, you can once again utilize individual and bulk tagging features to select and group the corresponding images. By tagging these outliers, you can conveniently organize and manipulate them for subsequent analysis and actions. Access the tagged image group at the bottom of the left sidebar in the Actions tab.  Act on Label Outliers Within the Actions tab, click "Filter data frame on" and select the "tags" option, allowing you to narrow down the data frame to focus solely on the tagged label outliers. From here, you can choose the desired actions, such as exporting the outliers, relabeling them, augmenting the dataset, reviewing them in detail, or even deleting them when necessary. How to Improve Training Data in Encord Active After reviewing the outlier detection procedure using Encord Active, let's examine the advantages it offers for enhancing training data.  Data Cleaning With its comprehensive set of tools for outlier detection, data visualization, and data quality assessment, Encord Active empowers users to identify problematic data points easily.  You can efficiently detect and address problematic data points early in your machine learning pipeline by leveraging these features. This proactive approach ensures that you can identify potential issues and mitigate them early on, leading to improved data quality and more reliable machine learning models. {{gray_callout_start}} 💡Encord allows you to filter data in 3 ways: Standard filter feature, embeddings plot, and natural language search. Click here to find out more. {{gray_callout_end}} Balancing Data Distribution The Actions tab in Encord Active offers two valuable options for refining your dataset: Filtering and creating a new dataset Balancing your existing dataset These options are accompanied by visualization capabilities, enabling you to make informed decisions about dataset quality without resorting to data augmentation or solely relying on the original dataset. By creating a small, balanced dataset through filtering, you can conduct thorough tests on your machine learning model. This approach helps you finalize your model in the pipeline, assess its performance, and determine if they require any further adjustments or augmentations. It also helps you evaluate different machine learning models. That enables you to make informed decisions about the most effective approach for your specific task. By leveraging the flexibility of the Actions tab in Encord Active and utilizing visualizations effectively, you can make data-driven choices regarding dataset balancing, augmentation, and model selection. This iterative process ensures that your training data is refined and optimized for the best possible model performance. {{gray_callout_start}} 💡To know more about the importance of data balancing, read the blog Introduction to Balanced and Imbalanced Datasets in Machine Learning {{gray_callout_end}} Continuous Iteration and Feedback Continuous iteration and refinement of training data based on model performance and feedback are crucial for achieving optimal results. Regularly evaluating the model's accuracy, identifying areas for improvement, and updating the training data accordingly are essential steps in this process.  Encord Active offers a range of tools to monitor model performance and assess the impact of data modifications, enabling informed decision-making. Model quality metrics provide a valuable means to evaluate data and labels using a trained model and imported predictions.  By leveraging these metrics, you can gain insights into the strengths and weaknesses of your dataset, enabling targeted improvements. Encord Active also helps with data versioning, allowing you to compare and assess the model's performance on each version. This iterative approach helps identify the best version of the dataset that yields optimal model performance. By leveraging the feedback loop between model evaluation, dataset refinement, and performance assessment in Encord Active, you can continuously optimize your training data to improve model quality. {{Training_data_CTA}} Conclusion Improving training data quality is crucial for boosting the accuracy and effectiveness of machine learning models. Outlier detection, as explored in this blog post, plays a vital role in identifying and managing data outliers and label outliers. Encord Active offers robust outlier detection features that enable users to easily spot significant deviations in data. By tagging and organizing outliers, Encord Active streamlines the workflow and ensures data quality through its intuitive interface and comprehensive toolset. However, the benefits of Encord Active extend beyond outlier detection. The platform empowers users to perform data cleaning, balance data distribution, and iterate on the dataset based on model performance and feedback. With data visualization, dataset filtering, and model quality metrics, users can make data-driven decisions and continually optimize their training data. By proactively addressing problematic data points, balancing data distribution, and iterating on the dataset, users can enhance the quality and reliability of their machine learning models. Encord Active serves as a powerful platform for these tasks, enabling users to refine and optimize their training data to achieve optimal model performance. Are you ready to improve your training data with Encord Active? Sign-up for a free trial of Encord: The Data Engine for AI Model Development, used by the world’s pioneering computer vision teams.  Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning.

Data labeling operations are integral to the success of machine learning and computer vision projects. Data operation teams manage the entire end-to-end lifecycle of data labeling, including data sourcing, cleaning, and collaborating with ML teams to implement model training, quality assurance, and auditing workflows. The scalability of these teams is crucial. Behind the scenes, data operations teams ensure that artificial intelligence projects run smoothly.  As computer vision, machine learning, and deep learning projects scale and data volumes expand, it is critical that data ops teams grow, streamline, and adapt to meet the challenge of handling more labeling tasks.  In this article, we will cover 6 steps that data operations managers need to take to scale their teams and operational practices. What is Data Labeling for Machine Learning and Computer Vision? Data labeling or data annotation ⏤ the two terms that are often used synonymously, ⏤ is the act of applying labels and annotations to unlabeled data for the purpose of machine learning algorithms. Labels can be applied to various types of data, including images, video, text, and voice. For the purpose of this article, we will focus on data labeling for computer vision use cases, in which labels are applied to images and videos to create high-quality training datasets for AI models. Data labeling tasks could be as simple as applying a bounding box or polygon annotation with “cat” label or as complicated as microcellular labels applied to segmentations of tumors for a healthcare computer vision project.  Regardless of complexity, accuracy is essential in the labeling process to ensure high-quality training datasets and to optimize model performance.  Data labeling can be time-consuming and expensive. As such, companies must weigh the advantages and disadvantages of outsourcing or hiring in-house. While outsourcing is often more cost-effective, it comes with quality control concerns and data security risks. And, while in-house teams are expensive, they guarantee higher labeling quality and real-time insight into team members labeling tasks.  The quality of training data directly impacts the performance of machine learning algorithms.,, Ultimately, it comes down to the labeling quality, a responsibility entrusted to data labeling teams.  High-quality data requires a quality-centric data operations process with systems and management that can handle large volumes of labeling tasks for images or videos. {{gray_callout_start}} 💡Find out more with Encord’s guides on How to choose the best datasets for machine learning and How to choose the right data for computer vision projects. {{gray_callout_end}}  Challenges of Scaling Data Labeling Operations Data labeling is a time-consuming and resource-intensive function. Data ops team members have to account for and manage everything from sourcing data to data cleaning, building and maintaining a data pipeline, quality assurance, and training a model using training, validation, and test sets.  Even with an automated data annotation tool, there is a lot for data ops managers to oversee.  There are several challenges that data labeling teams face when scaling: Project resources: Scaling requires additional resources and funding. Determining the best allocation of both can be a challenge Hiring and training: Hiring and training new team members require time and resources to align with project requirements and data quality standards. This forces teams to consider the options of outsourcing or managing teams in-house?  Quality control: As the volume of data increases, maintaining How do we maintain high-quality labels becomes challenging.  Workflow and data security: As data labeling tasks increase, it can be challenging  to maintain data security, compliance, and audit trails. Annotation software: As image and video volumes increase, it can be challenging to manage projects. It is imperative to use the right tools, as teams can often benefit from the automation of data labeling tasks. Let’s look at how to solve these challenges.  6 Best Practices to Implement Scalable Data Labeling Operations Data operations teams are crucial for supporting data scientists and engineers. Here are 6 best practices for managing and implementing data labeling operations at scale. 1. Design a workflow-centric process Designing workflow-centric processes is crucial for any AI project. Data ops managers need to establish the data labeling project’s processes and workflows by creating standard operating procedures.  {{gray_callout_start}} 💡For more information, read Best Practice Guide for Computer Vision Data Operations Teams {{gray_callout_end}}  The support of senior leadership is vital to obtain the resources and budget to grow the data ops team, use the right tools, and employ a workforce for data labeling that can handle the volume needed. 2. Select an effective workforce for data labeling To select the appropriate workforce for data labeling operations, there are three options available: an in-house team, outsourced labeling services, or a crowd-sourced labeling team.  The choice depends on several factors:  Data volume Specialist knowledge Data security  Cost considerations Management In many cases, the benefits of using outsourced labeling service providers outweigh the associated risks and costs. In regulated sectors like healthcare, however, the use of in-house teams is often the only option given data security concerns and the highly specialized knowledge required.  Crowdsourcing through platforms like Amazon Mechanical Turk (MTurk) and SageMaker Ground Truth is another viable option for computer vision projects. Proper systems and processes, including workforce and workflow management and annotator training, are essential to the success of crowdsourcing or outsourcing.  3. Automate the data labeling process Similar to the staffing question, there are three options for automating data labeling: in-house tools, open-source, or commercial annotation solutions such as Encord. Open-source data labeling tools are suitable for projects with limited funding, such as academia or research, or for when a small team is building an MVP (minimum viable product) version of an AI model. These tools, however, often don’t meet the requirements for large-scale commercial projects. Developing an in-house tool can be a time-consuming and costly endeavor, taking 9 to 18 months and involving significant R&D expenses. In contrast, an off-the-shelf labeling platform can be quickly implemented. While pricing is higher than open-source (usually free for basic versions), it is cheaper than building an in-house data labeling tool. With an AI-assisted labeling and annotation platform, such as Encord, data ops teams can manage and scale the annotation workflows. The right tool also provides quality control mechanisms and training data-fixing solutions.  4. Leverage software principles for DataOps  Software development principles can be leveraged when scaling data labeling and training for a computer vision project.  Since data engineers, scientists, and analysts often engage in code-intensive tasks, integrating practices like continuous integration and delivery (CI/CD) and version control into data ops workflows is logical and advantageous.  {{try_encord_CTA_annotate_visual}} 5. Implement quality assurance (QA) iterative workflows  To ensure quality control and assurance at scale, it is crucial to establish a fast-moving and iterative process. One effective approach is to establish an active learning pipeline and dashboard. This allows data ops leaders to maintain tight control over quality at both a high-level and individual label level.  {{gray_callout_start}} 💡Here are our guides on 5 Ways to Improve The Quality of Data Labels and an Introduction to Quality Metrics in Computer Vision {{gray_callout_end}}  6. Ensure transparency and audibility in the data and labeling pipeline Label transparency and audibility are essential throughout the data pipeline.  A clear, user-logged, and timestamped audit trail is critical for projects in secure or regulated sectors like healthcare where  FDA compliance is required. With new AI laws likely to come into force worldwide in the next few years, a data labeling audit trail could also become mandatory for commercial AI models in non-regulated industries.   {{gray_callout_start}} 💡 Find out more with our Best Practice Guide for Computer Vision Data Operations Teams {{gray_callout_end}}   Scaling Data Labeling Operations: Key Takeaways  High-quality training datasets are essential for optimizing model performance. The function of data operations teams is to ensure the labeling quality and labeling workflow are smooth and frictionless.  Follow these 6 best practices to scale your data operations properly:  Design workflow-centric processes Select an effective workforce for data labeling  Automate the data labeling process Leverage software principles for DataOps  Implement QA iterative workflows  Ensure transparency and audibility in the data and labeling pipeline With an AI-powered annotation platform, data ops managers can oversee complex workflows, make annotation more efficient, and achieve labeling quality and productivity targets.   Are you ready to scale your data labeling operations and need a powerful AI-based software suite for computer vision projects?  Sign-up for a free trial of Encord: The Data Engine for AI Model Development, used by the world’s pioneering computer vision teams.  Follow us on Twitter and LinkedIn for more content on computer vision, training data, and active learning.

Data labeling operations are an essential component of training and building a computer vision model. Data operations are a function that oversees the full lifecycle of data labeling and annotation, from sourcing and cleaning through to training and making a model production-ready.  Data scientists and machine learning engineers aren’t wizards. Getting computer vision projects production-ready involves a lot of hard work, and behind the scenes are tireless professionals known as data operations teams.  Data operations, also known as data labeling operations teams, play a mission-critical role in implementing computer vision artificial intelligence projects.  Especially when a project is data-centric. It’s important and helpful to have an automated, AI-backed labeling and annotation tool, but for a project to succeed, you also need a team and process to ensure the work goes smoothly.  In other words, to ensure data labels and annotations are high-quality, a data labeling operations function is essential.  In this article, we will cover:  Why data labeling operations are crucial for any algorithmic learning project (e.g., CV, ML, AI, etc.)? What are the benefits of data labeling operations?  Should you buy or build data labeling and annotation software?  5 strategies for creating successful data labeling operations  Let’s dive in . . .  Why do Computer Vision Projects Need Data Labeling? Data labeling, also known as data annotation, is a series of tasks that take raw, unlabeled data and apply annotations and labels to image or video-based datasets (or other sources of data) for computer vision and other algorithmic models.  Quality and accuracy are crucial for computer vision projects. Inputting poor-quality, badly labeled, and annotated images or videos will generate inaccurate results.  Data labeling can be implemented in a number of ways. If you’ve only got a small dataset, your annotation team might be able to manage using manual annotation. Going through each image or video frame one at a time.  However, in most cases, it helps to have an automated data annotation tool and to establish automated workflows to accelerate the process and improve quality and accuracy.  Why are Data Labeling Operations Mission-critical? An algorithmic model's performance is only as effective as the data it’s trained on.  Dozens of sectors, including medical and healthcare, manufacturing, and satellite images for environmental and defense purposes, rely on high-quality, highly-accurate data labeling operations. Annotations and labels are how you show an algorithmic model, including computer vision models and what’s in images or videos. Algorithms can’t see. We have to show them. Labels and annotations are how humans train algorithms to see, understand, and contextualize the content/objects within images and videos.  Data labeling operations make all of this possible. There’s a lot of work that goes into making data training and then production-ready, including data cleaning tasks, establishing and maintaining a data pipeline, quality control, and checking models for bias and error.  {{check_out_on_github_visual}} It all starts with sourcing the data. Either this is proprietary or can come from open-source datasets. {{gray_callout_start}}Here’s our guides for:  How to choose the best datasets for machine learning.  How to choose the right data for computer vision projects.{{gray_callout_end}}  What Are The Benefits of Data Labeling Operations? There are numerous benefits to having a highly-skilled and smoothly-ran data labeling operations team, such as:  Improved accuracy and performance of machine learning and computer vision models, thanks to higher-quality training data going into them.  Reduced time and cost of implementing full-cycle data labeling and annotations.  Improved quality of training data, especially when data ops is responsible for quality control and iterative learning and applies automated tools using a supervised or semi-supervised approach.  An effective data ops team ensures a smooth and unending flow of high-quality data, helping to train a computer vision model.  With the right data ops team, you can make sure the machine learning ops (MLops) team is more effective, supporting model training to produce the desired outcomes.  Buy vs. Build for Data Labeling Ops Tools? Whether to buy or build data labeling tools is a question many teams and project leaders consider.  It might seem an advantage to develop your own data labeling and annotation software. However, the downsides are that it’s a massively expensive and time-consuming investment.  Having developed in-house software, you will need engineers to maintain and update it. And what if you need new features? Your ability to scale and adapt is more restricted.  There are open-source tools and lots of them. However, for commercial data ops teams, most don’t meet the right requirements.  Compared to building your own solution, buying/signing-up for a commercial platform is several orders of magnitude more cost and time-effective. Plus, you can be up and running in a matter of days, even hours, compared to 9 to 18 months if you go the in-house route.  {{try_on_github_bar}} For projects that need tools for specific use cases, such as collaborative annotating tooling for clinical data ops teams and radiologists, there are commercial platforms on the market tailored for numerous industries, including healthcare.  5 Strategies to Create Successful Data Labeling Operations  Now let’s look at 5 strategies for creating successful data labeling operations.  Understand the use case Before launching into a project, data ops and ML leaders need to understand the problems they’re trying to solve for the particular use case.  It’s a helpful exercise to map out a series of questions and work with senior leadership to understand project objectives and the routes to achieving them successfully. {{gray_callout_start}}Begin the process of establishing data operations by asking yourself the following questions:  What are the project objects?  How much data and what type of data does this project need?  How accurate does the model have to be when it’s production-ready to achieve the objectives?  How much time does the project have to achieve the goals?  What outcomes are senior leadership expecting?  Is the allocated budget and resources sufficient to produce the results senior leaders want?  If not, how can we argue the business case to increase the budget if needed?  What’s the best way to implement data labeling operations: In-house, outsourced, or crowd-sourced?{{gray_callout_end}}  Once you’ve worked through the answers to these questions, it’s time to get build a data labeling operations team, processes, and workflows.  Document labeling workflows and create instructions Taking a data-centric approach to data ops means that you can treat datasets, including the labels and annotations, as part of your organization's and project's intellectual property (IP). Making it more important to document the entire process.  Documenting labeling workflows means that you can create standard operating procedures (SOPs), making data ops more scalable. It’s also essential for safeguarding datasets from data theft and cyberattacks and maintaining a clearly auditable and compliant data pipeline.  Designing operational workflows before a project starts is essential. Otherwise, you’re putting the entire project at risk once data starts flowing through the pipeline.  Create clear processes. Get the tools you need, budget, senior leadership buy-in, and resources, including operating procedures, before the project starts.  {{try_encord_today_bar}} Plan for the long haul (make your ontology expandable) Whether the project involves video annotation or image annotation or you’re using an active learning pipeline to accelerate a model’s iterative learnings, it’s important to make your ontology expandable.  Regardless of the project, use case, or sector, including whether you’re annotating medical image files, such as DICOM and NIfTI, an expandable ontology means it’s easier to scale.  Getting the ontology and label structure right at the start is important. No matter which approaches you take to labeling tasks or whether you automate them, everything flows from the labels and ontology you create.  Start small and iterate The best way to build a successful data labeling operations workflow is to start small, learn from small setbacks, iterate, and then scale. Otherwise, you risk trying to annotate and label too much data in one go. Annotators make mistakes, meaning there will be more errors to fix. It will take you more time to attempt to annotate and label a larger dataset, to begin with, than to start on a smaller scale.  Once you’ve got everything running smoothly, including integrating the right labeling tools, then you can expand the operation. {{try_encord_CTA_annotate_visual}} Use iterative feedback loops, implement quality assurance, and continuously improve Iterative feedback loops and quality assurance/control are an integral part of creating and implementing data operations.  Labels need to be validated. You need to make sure annotation teams are applying them correctly. Monitor for errors, bias, and bugs in the model. It’s impossible to avoid errors, inaccuracies, poorly-labeled images or video frames, and bugs.  With the right AI-powered, automated data labeling and annotation tool, you can reduce the number and impact of errors, inaccuracies, poorly-labeled images or video frames, and bugs in training data and production-ready datasets.  Pick an automation tool that integrates into your quality control workflows to ensure bugs and errors are fixed faster. This way, you’ll have more time and cost-effective feedback loops, especially if you’ve deployed an automated data pipeline, active learning pipelines, or micro-models.  Build More Streamlined and Effective Data Label Operations With Encord With Encord and Encord Active, automated tools used by world-leading AI teams, you can build data labeling operations more effectively, securely, and at scale.  Encord was created to improve the efficiency of automated image and video data labeling for computer vision projects. Our solution also makes managing data ops and a team of annotators easier, more time, and cost-effective while reducing errors, bugs, and bias.  Encord Active is an open-source active learning platform of automated tools for computer vision: in other words, it's a test suite for your labels, data, and models. With Encord, you can achieve production AI faster with ML-assisted labeling, training, and diagnostic tools to improve quality control, fix errors, and reduce dataset bias.  Make data labeling more collaborative, faster, and easier to manage with an interactive dashboard and customizable annotation toolkits. Improve the quality of your computer vision datasets, and enhance model performance.  Key Takeaways: How to build successful data labeling operations  Building a successful data labeling operation is essential for the success of computer vision projects. It takes time, work, and resources. But once you’ve got the people, processes, and tools in place, you can take data operations to the next level and scale computer vision projects more effectively.  Successful data operations need the following scalable processes:  Project goals and objectives;  Documented workflows and processes;  An expandable ontology;  Iterative feedback loops and quality assurance workflows;  And the right tools to make everything run more smoothly, including automated, AI-based annotation and labeling.  Ready to improve the performance and scale your data operations, labeling, and automated annotation?  Sign-up for an Encord Free Trial: The Active Learning Platform for Computer Vision, used by the world’s leading computer vision teams.  AI-assisted labeling, model training & diagnostics, find & fix dataset errors and biases, all in one collaborative active learning platform, to get to production AI faster. Try Encord for Free Today.  Want to stay updated? 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