Discover the 9 most popular free and paid image annotation tools to know about heading into 2024. Compare their features and pricing, and choose the best image labelling tool for your use case. Annotating images is still one of the most time-consuming steps in bringing a computer vision project to market. To help you out, we compiled a list of the most popular image annotation tools out there. Whether you are: A computer vision team building unmanned drones with your own in-house annotation tool A data science team working on a large-scale defect inspection model looking for labelling services Or a data operations team looking for the right platform for your radiologists to accurately label CT scans This guide will help you compare the top image annotation tools and find the right one for you. We will compare each based on key factors - including image annotation functionality, support for different data types and use cases, QA/QC capabilities, security and data privacy, data management, integration with the machine learning pipeline, and customer support. We’ll be updating this review periodically with notable feature releases and the latest image annotation tools coming to market. So let's get to it! The 9 most popular image annotation tools:  Encord Annotate Scale CVAT Labelbox Playment Appen Dataloop V7 Labs Hive {{light_callout_start}} Exploring image annotation tools? Label data 10x faster and gain control of your training data with Encord - get in touch to start today. {{light_callout_end}} Encord Annotate Encord Annotate is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management. It's the best option for teams that are: Looking for automated, semi-automated or AI-assisted image and video annotation. Annotating all modalities (DICOM and NIfTI, SAR, ultra-high-resolution, and more). Wanting one place to easily manage annotators, track performance, and create QA/QC workflows. Benefits & Key features: Use-case-centric annotations — from native DICOM & NIfTI annotations for medical imaging to SAR-specific features for geospatial data. Support for all annotation types — bounding boxes, polygons, polylines, image segmentation, keypoint, custom object primitives (rotating bounding boxes, 3D cuboids), and more. Incorporates auto-annotation tools such as Meta's Segment Anything Model and other AI-assisted labeling techniques. Integrated data labeling services. Integrated MLOps workflows for computer vision and machine learning teams — to detect edge cases and gaps in your training data and generate augmented data to improve label quality. Easy collaboration, annotator management, and QA workflows — to track annotator performance and increase label quality. Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance. Advanced Python SDK and API access (+ easy export into JSON and COCO formats). Best for teams who: Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale your annotation workflows. Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies or rendering DICOM formats natively). Pricing: Free trial model with simple per-user pricing thereafter {{Training_data_CTA::Label data 10x faster and gain control of your training data}} Scale Scale AI, now Scale, is a data and labeling services platform that supports image, audio, text, and video annotations. As of 2023, Scale has also expanded into new use cases, like user experience optimization, large language models, and synthetic data. Benefits & Key features: Leader in workforce management Support for multiple data modalities (image, video, document processing, audio and more) Nucleus dataset management Best for: Workforce management Pricing: On a per-image basis 💡 More insights on data labeling with Scale: Teams looking for annotation tools for Autonomous Vehicle vision should know… Scale is one of the earliest platforms on the market to support 3D Sensor Fusion annotation for RADAR and LiDAR use cases, and their capabilities are some of the most widely adopted. Teams looking for medical imaging annotation tools should know… Platforms like Scale will usually not support DICOM or NIfTI data types nor allow companies to work with their own annotators on the platform. So if you’re looking to annotate medical images, like CT, X-Ray, or MRI scans, you’ll want to look for a medical purpose-built platform (built for radiologist or physician annotators and specialist formats like DICOM and NIfTI) like Annotate. Teams looking for labeling services should know… Scale is a very popular option for data labeling services. Other alternatives (discussed later in this article) are Appen and Playment. CVAT CVAT (Computer Vision Annotation Tool) is a free, open-source, 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. Benefits & Key features: Easy (and free) to get started labeling images with Great for manual data annotation — also supports semi-assisted labeling Robust ground-level annotation capabilities (including classification and object detection) for a broad set of computer vision use cases Best for: Students, researchers, and academics testing the waters with image annotation (perhaps with a few images or a small dataset) Pricing: Free! 💡 More insights on image labeling with CVAT: If your team is looking for a free annotation tool, you should know… 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: Buy vs build for computer vision annotation - what's better? Overview of open source annotation tools for computer vision Complete guide to image annotation for computer vision 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 labelling platform, as well as labelling services. Teams can annotate a wide range of data types (PDF, audio, images, videos, and more). Benefits & Key features: Flexible QA workflows and annotator performance tracking Integrated 3rd party labeling services through Labelbox Boost Model-assisted annotations Robust support for multiple data types beyond images, especially text Best for: Teams looking for a powerful platform to quickly annotate documents and text 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. 💡 More insights on image labeling with Labelbox: Teams looking for document processing annotation should know that… Labelbox has been heavily investing in its Document and Conversational AI products, released in 2023. Its document annotation capabilities are quickly gaining traction, particularly within the financial services industry.  Teams carrying out annotation projects that are use-case specific should know that… 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 image annotation platforms will be a better fit. 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 high-quality training data for various use cases, supported by manual labelers and a machine learning platform. Benefits & Key features: One of the largest global workforces of contractors and data labelers (+1M annotators) Human-assisted 2D and 3D functionality for image annotations Speech recognition training platform (handles all data types across 500+ languages and dialects) Best for: Teams looking for a fully managed solution to get labeled data from (just share data and label guidelines) 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. Benefits & Key features: Support for multiple annotation types (bounding boxes, polygons, and image segmentation) Data sourcing (pre-labeled datasets), data preparation, and real-world model evaluation Natural language processing and functionality for broader text-to-speech support are available 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 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. Benefits & Key features: Features for image annotation tasks, including classification, detection, and semantic segmentation Support for video annotations Intuitive and easy-to-use user interface Best for: Teams looking for a powerful platform to annotate wide variety of data types Pricing: Free trial and an enterprise plan 💡 More insights on image labeling with Dataloop: Teams carrying out specific image and video annotation projects that are use-case specific should know that… 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).  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. Benefits & Key features: Robust project management and automation workflow functionality, with real-time collaboration and tagging Integrated labeling services 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) 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’s platform allows engineers to bring their own pre-trained ML models into the platform, and leverage them for content moderation (including detection of AI-generated data). Benefits & Key features: End-to-end image annotation tool Support for unique image annotation use cases (ad targeting, semi-automated logo detection) Flexible access to model predictions with a single API call Bring your own AI model (BYOM) Best for: Teams labeling images and other data types for the purpose of content moderation. Hive is especially popular with social media platforms like Reddit, BeReal, and Quora. Pricing: Enterprise plan Conclusion There you have it! The 9 Best Image Annotation Tools for computer vision in 2023.  For further reading, you might also want to check out a few 2023 honorable mentions, both paid and free annotation tools: Labelstudio - user-friendly open source tool praised for its manual annotation process capabilities. Supervisely - commercial data labeling platform praised for its quality control functionality and basic interpolation feature. VoTT - open source tool, praised for its tags and assets export to Tensorflow (PascalVOC) and YOLO format. {{try_encord_CTA_annotate_visual}}

Are you looking for a video annotation tool for your computer vision project? Look no further! We've compiled a list of the top eight best video annotation tools, complete with their use cases, benefits, key features, and pricing. Deciding on the right video annotation toolkit for your needs depends on several factors, including whether you have vast amounts of unlabeled data and whether manual annotation is too time-consuming and expensive. With a powerful video annotation tool, you can automate and accelerate the process. Our list is designed for data ops teams looking to manage in-house or outsourced annotators, CTOs hoping to reduce the cost of manual annotation, and data scientists and ML engineers in search of a solution to automate annotations and labeling while identifying potential edge cases and outliers. {{light_callout_start}} Working with images? Check out our 9 Best Image Annotation Tools for Computer Vision instead! {{light_callout_end}} Top 8 Video Annotation Tools for Computer Vision Encord LabelMe CVAT SuperAnnotate Dataloop Supervisely Scale Img Lab  Let’s dive in ... Encord Encord's collaborative video annotation platform helps you label video training data more quickly, build active learning pipelines, create better-quality datasets and accelerate the development of your computer vision models. Encord's suite of features and toolkits includes an automated video annotation platform that will help you 6x the speed and efficiency of model development. Encord is a powerful solution for teams that:  Need a native-enabled video annotation platform with features that make it easy to automate the end-to-end management of data labeling, QA workflows, and automated AI-powered annotation Want to accelerate their computer vision model development, making video annotation 6x faster than manual labeling. Benefits & key features:  Encord is a state-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models, ideal for video annotation, labeling, QA workflows, and training computer vision models Built for computer vision, with native support for numerous annotation types, such as bounding box, polygon, polyline, instance segmentation, keypoints, classification, and much more  As a computer vision toolkit, it supports a wide-range of native and visual modalities for video annotation and labeling, including native video file format support (e.g., full-length videos, and numerous file formats, including MP4 and WebM) Automated, AI-powered object tracking means your annotation teams can annotate videos 6x faster than manual processes Assess and rank the quality of your video-based datasets and labels against pre-defined or custom metrics, including brightness, annotation duplicates, occlusions in video or image sequences, frame object density, and numerous others  Evaluate training datasets more effectively using a trained model and imported model predictions with acquisition functions such as entropy, least confidence, margin, and variance with pre-built implementations Manage annotators collaboratively and at scale with customizable annotator and data management dashboards Best for:  ML, data ops, and annotation teams looking for a video annotation tool that will accelerate model development. Data science and operations teams that need a solution for collaborative end-to-end management of outsourced video annotation work. Pricing:  Start with a free trial or contact sales for enterprise plans. Further reading:  The Complete Guide to Image Annotation for Computer Vision 4 Ways to Debug Computer Vision Models [Step By Step Explainer] Closing the AI Production Gap with Encord Active Active Learning in Machine Learning: A Comprehensive Guide {{Training_data_CTA::Label 10x faster with the leading video annotation tool}} LabelMe LabelMe is an open-source online annotation tool developed by the MIT Computer Science and Artificial Intelligence Laboratory. It includes the downloadable source code, a toolbox, an open-source version for 3D images, and image datasets you can train computer vision models on. LabelMe Benefits & key features:  LabelMe includes a dataset you can use to train models on, and you can use the LabelMe Matlab toolbox to annotate and label them (here’s the Github repository for this) It also comes with a 3D database with thousands of images of everyday scenes and object categories  You can also outsource annotation using Amazon Mechanical Turk, and LabelMe encourages this here. Best for:  ML and annotation teams. Although, given the open-source nature of LabelM and the database, it may be more effective and useful for academic rather than commercial computer vision projects.  Pricing:  Free, open-source.  CVAT CVAT (Computer Vision Annotation Tool) started life as an Intel application that they made open-source, thanks to an MIT license. Now it operates as an independent company and foundation, with Intel’s continued support under the OpenCV umbrella.  CVAT.org has moved to its new home, at CVAT.ai.  CVAT Benefits & key features:  CVAT is now part of an extensive OpenCV ecosystem that includes a feauture-rich open-source annotation tool With CVAT, you can annotate images and videos by creating classifications, segmentations, 3D cuboids, and skeleton templates Over 1 million people have downloaded it since CVAT launched, and under OpenCV, there’s an even larger community of users to ask for guidance and support.  Best for:  Data ops and annotation teams that need access to an open-source tool and ecosystem of ML engineers and annotators.  Pricing:  Free, open-source.  SuperAnnotate SuperAnnotate is a commercial platform and toolkit for creating annotations and labels, managing automated annotation workflows, and even generating images and datasets for computer vision projects. SuperAnnotate Benefits & key features:  SuperAnnotate includes a full-service Data Studio, including access to a marketplace of 400+ outsourced annotation teams and service providers  It also comes with an ML Studio to manage computer vision and AI-based workflows, including AI data management and curation, MLOps and automation, and quality assurance (QA) It’s designed for numerous use cases, including healthcare, insurance, sports, autonomous driving, and several others.  Best for:  ML engineers, data scientists, annotation teams, and MLOps professionals in academia, businesses, and enterprise organizations.  Pricing: Free for early-stage startups and academic researchers. You would need a demo or contact sales for the Pro and Enterprise plans.   Dataloop Dataloop is a "data engine for AI" that includes automated annotation for video datasets, full lifecycle dataset management, and AI-powered model training tools. Dataloop Benefits & key features:  Multiple data types supported, including numerous video file formats Automated and AI-powered data labeling End-to-end annotation and QA workflow managment and dashboards for collaborative working Best for:  ML, data ops, enterprise AI teams, and managing video annotation workflows with outsourced teams. Pricing:  From $85/mo for 150 annotation tool hours. Supervisely Supervisely is a "Unified OS enterprise-grade platform for computer vision" that includes video annotation tools and features. Supervisely Benefits & key features:  Native video file support, so that you don't need to cut them into segments or images Automated multi-track timelines within videos Built-in object tracking and segments tagging tools, and numerous other features for video annotation, QA, collaborative working, and computer vision model development Best for:  ML, data ops, and AI teams in Fortune 500 companies and computer vision research teams. Pricing:  30-day free trial, with custom plans after signing-up for a demo. Scale Scale is positioned as the AI data labeling and project/workflow management platform for “generative AI companies, US government agencies, enterprise organizations, and startups.”  Building the best AI, ML, and CV models means accessing the “best data,” and for that reason, it comes with tools and solutions such as the Scale Data Engine and Generative AI Platform.  Scale, an enterprise-grade data engine and generative AI platform Benefits & key features:  A Data Engine to unlock data organizations already have or can tap into vast public and open-source datasets  Tools to create synthetic data (e.g., generative AI features) A full-stack Generative AI platform for AI companies and US government agencies  An extensive developers platform for Large Language Model (LLM) applications.  Best for:  Data scientists and ML engineers in generative AI companies, US government agencies, enterprise organizations, and startups.  Pricing:  There are two core offerings: Label My Data (priced per-label), and an Enterprise plan that requires a demo to secure a price. Img Lab Img Lab is an open-source image annotation tool to “simplify image labeling/ annotation process with multiple supported formats.”  Img Lab Benefits & key features:  Img Lab isn’t as feature-rich as most of the tools and platforms on this list. It would need to be integrated with other tools and applications to ensure it could be used effectively for large-scale image annotation projects.  Best for:  Img Lab seems best equipped for annotators and those who need a quick and easy-to-use open-source annotation tool.  Pricing:  Free, open-source.   How To Pick the Best Video Annotation Tool for Computer Vision Projects? And there we go, the best video annotation tools for computer vision! In this post, we covered Encord, LabelMe, CVAT, SuperAnnotate, Dataloop, Supervisely, Scale, and Img Lab.  Each tool and suite of features that are included are applicable to a wide-range of use cases, data types, and project scales. Making the right choice depends on what your computer vision project needs, such as supporting various data modalities and annotation types, active learning strategies, and pricing.  When you’ve selected the best annotation tool for your project or AI application will accelerate model development, enhance the quality of your training data, and optimize your data labeling and annotation process.  {{Training_data_CTA}}

Data contains hidden insights that completely alter how we make business decisions. However, data often consists of abnormal instances, known as outliers, that can distort the outcome of data processing and analysis. Moreover, machine learning (ML) models trained using data with outliers may have suboptimal predictive performance. Hence, outlier detection is a crucial step in any data pipeline.   Here's the catch: manually identifying data outliers is difficult and time-consuming, especially for large datasets. As a result, data scientists and artificial intelligence (AI) practitioners employ outlier detection tools to quickly identify outliers and streamline their data processing and ML pipelines. In this guide, we’ll explore outlier detection techniques and list the top tools that can be utilized for this purpose. These include: Encord Active Lightly Aquarium Voxel Deepchecks Arize Outlier Detection: Types & Methods Outliers are data points with extreme values that are at disproportionately large distances from the normal distribution of the dataset. They represent an abnormal pattern compared to the regular data points. They can occur for various reasons, including data entry and label errors, measurement discrepancies, missing values, and rare events. There are three main types of outliers: Global or Point Outliers: Individual data points that deviate significantly from the normal distribution of the dataset. Contextual Outliers: Data points with abnormal distances within a specific context or subset of the data.  Collective Outliers: Groups or subsets of data that exhibit unusual patterns compared to the entire dataset.  Outliers are also classified based on the number of variables. These are:  Univariate Outliers: Data points of a single variable that are distant from regular observations. Multivariate Outliers: A combination of extreme data values on two or more variables. Illustration of outliers in 2D data Now, let’s explore some common outlier detection methods that AI practitioners use: Z-score Method This method identifies outliers based on the number of standard deviations from the mean. In other words, the z-score is a statistical measurement that determines how distant a data point is from its distribution. Typically, a data point with a Z-score beyond +3 or -3 is considered an outlier. The Z-score results are best visualized with histograms and scatter plots. Clustering Method This method identifies various data clusters in the dataset distribution using techniques like:  K-means clustering, a technique that creates clusters of similar data points, where each cluster has a centroid (center points or cluster representatives within a dataset), and data points within one cluster are dissimilar to the data points in another cluster.  Density-based spatial clustering of applications with noise (DBSCAN) to detect data points that are in areas of low density (where the nearest clusters are far away)  In such methods, outliers are identified by calculating the distance between each data point and the centroid, and data points that are farthest from the cluster centers are typically categorized as outliers. The clustering results are best visualized on scatter plots. Interquartile range (IQR) Method  This method identifies outliers based on their position in relation to the data distribution's percentiles. The IQR is calculated as the difference between the third quartile (Q3) and first quartile (Q1) in a rank-ordered portion of data. Typically, an outlier is identified when a data point is more than 1.5 times the IQR distance from either the lower (Q1) or upper quartile (Q3). The IQR method results are best visualized with box plots. Many outlier detection tools use similar or more advanced methods to quickly find anomalies in large datasets. And there are many out there. How can you pick the one that best suits your requirements?  Let’s compare our curated list of top outlier detection tools to help you find the right one. Our comparison will be based on key factors, including outlier detection features, support for data types, customer support, and pricing. Encord Active Encord Active is a powerful active learning toolkit for advanced error analysis for computer vision data to accelerate model development. Encord Active dashboard Benefits & Key Features Surface and prioritize the most valuable data for labeling Search and curate data across images, videos, DICOM files, labels, and metadata using natural language search Auto-find and fix dataset biases and errors like outliers, duplication, and labeling mistakes Find machine learning model failure modes and edge cases Employs precomputed interquartile ranges to process visual data and uncover anomalies Integrated tagging for data and labels, including outlier tagging Export, re-label, augment, review, or delete outliers from your dataset Employs quality metrics (data, label, and model) to evaluate and improve ML pipeline performance across several dimensions, like data collection, data labeling, and model training.  Integrated filtering based on quality metrics Supports data types like jpg, png, tiff, and mp4 Supports label types like bounding boxes, polygons, segmentation, and classification Advanced Python SDK and API access to programmatically access projects, datasets, and labels Provides interactive visualizations, enabling users to analyze detected outliers comprehensively Offers collaborative workflows, enabling efficient teamwork and improved annotation quality Best for Teams Who Are looking to upgrade from in-house solutions and require a reliable, secure, and collaborative platform to scale their anomaly detection workflows effectively. Need a suite of powerful tools to work on complex computer vision use cases across verticals like smart cities, AR/VR, autonomous transportation, and sports analytics. Haven't found an anomaly detection platform that aligns perfectly with their specific use case requirements {{light_callout_start}} Read our step-by-step guide to Improving Training Data with Outlier Detection with Encord {{light_callout_end}} Pricing There are two core offerings: a free, open-source version, and a team plan which requires a support contact. {{Training_data_CTA::Elevate model development with Encord Active’s advanced tools for outlier and issue detection}} Lightly Lightly is a data curation software for computer vision that offers improved model accuracy by utilizing active learning to find clusters or subsets of high-impact data within your training dataset. Lightly dashboard Benefits & Key Features Data selection is done via active and self-supervised learning algorithms based on three input types: embeddings, metadata, and predictions. Automates image and video data curation at scale to mitigate dataset bias Built-in capability to check for corrupt images or broken frames Data drift and model drift monitoring Python SDK to integrate with other frameworks and your existing ML stack using scripts LightlyWorker tool – a docker container to leverage GPU capabilities Best for Teams Who Require GPU capabilities to curate large-scale vision datasets, including special data types like LIDAR, RADAR, and medical. Want a collaborative platform for dataset sharing Pricing Lightly offers free community and paid versions for teams and custom plans. Aquarium Aquarium is an ML data operations platform that allows data management with a focus on improving training data. It utilizes embedding technology to surface problems in model performance.  Aquarium dashboard Users can upload streaming datasets into Aquarium's data operations platform. It retains the history of changes, enabling users to analyze the evolution of the dataset over time and gain insights.  Benefits & Key Features Generate, process, and query embeddings to find clusters of high-quality data from unlabeled datasets Allows for a variety of data to be curated, including images, 3D data, audio, and text Integrates with data labeling suppliers and ML tools like TensorFlow, Keras, Google Cloud, Azure, and AWS Inspects data and labels using visualization to find errors and bad data quickly Automatically analyze and calculate model metrics to identify erroneous data points Community and shared Slack channel support, as well as solution engineering assistance Best for Teams Who Require integration of vendor systems with a data operations platform enabling efficient data flow Need ML team collaboration on data curation and evaluation tasks {{light_callout_start}} Interested in learning more about the role of data operations? Read our comprehensive Best Practice Guide for Computer Vision Data Operations Teams. {{light_callout_end}}  Pricing Aquarium offers a free tier for a single user. They also offer team, business, and enterprise tiers for multiple users. Voxel51 Voxel51 is an open-source toolkit for curating high-quality datasets and building computer vision production workflows. FiftyOne dashboard Benefits & Key Features Integrates with ML tools to annotate, train, filter, and evaluate models Identifies your model’s failure modes Removes redundant images from training data Finds and corrects label mistakes to curate higher-quality datasets Dedicated slack channel for customer support Best for Teams Who Want to start with open-source tooling  Require a graphical user interface that enables them to visualize, browse, and interact directly with their datasets Pricing There are two core offerings: FiftyOne, a free, open-source platform, and FiftyOne Teams plan, which requires a support contact. Deepchecks Deepchecks is an ML platform and Python library for deep learning model monitoring and debugging. It offers validation of machine learning algorithms and data with minimal effort in the research and production phases. Deepchecks dashboard The Deepchecks tool utilizes the LoOP algorithm, a method for detecting outliers in a dataset across multiple variables by comparing the density in the area of a sample with the densities in the areas of its nearest neighbors.  Benefits & Key Features Utilizes Gower distance with LoOP algorithm to identify outliers Real-time monitoring of model performance and metrics (such as label drift) Provides Role-Based Access Control (RBAC) Prioritizes data privacy by encrypting data during transit and storage Slack community and Enterprise support for users Best for Teams Who Are required to monitor model performance and find and resolve production issues Deal with sensitive data and value a secure deployment {{light_callout_start}} Want to learn how to handle data pipelines at scale? Read our explanatory post on How Automated Data Labeling is Solving Large-Scale Challenges. {{light_callout_end}}  Pricing Deepchecks offers open-source and paid plans depending on the team’s security and support requirements. Arize Arize is an ML observability platform to help data scientists and ML engineers detect model issues, fix their underlying causes, and improve model performance. It allows teams to monitor, detect anomalies, and perform root cause analysis for model improvement. Arize dashboard It has a central inference store and comprehensive datasets indexing capabilities across environments (training, validation, and production), providing insights and making it easier to troubleshoot and optimize model performance. Benefits & Key Features Detect model issues in production Uses Vector Similarity Search to find problematic clusters containing outliers to fine-tune the model with high-quality data Automatic generation and sorting of clusters with semantically similar data points Best for Teams Who: Require real-time model monitoring for immediate feedback on model prediction and forecasting outcomes Pricing Arize offers a free tier for individuals and paid plans for small and global teams. What Should You Look For in an Outlier Detection Tool? Outlier detection is a crucial step in machine learning for ensuring data quality, accurate statistics, and reliable model performance. Various tools utilize different outlier detection algorithms and methods, so selecting the best tool for your dataset is essential. Consider the following factors when selecting an outlier detection tool: Ease of Use: Choose a user-friendly outlier identification solution that allows data scientists to focus on insights and analysis rather than a complex setup. Scalability: Select a solution that can efficiently handle enormous datasets, enabling real-time detection. Flexibility: Choose a platform that provides customizable options tailored to your unique data and outlier analysis use cases. This is essential for optimal performance. Visualizations: Select a platform that delivers clear and interactive visualizations to help you easily understand and analyze outlier data. Integration: Choose a tool that connects effortlessly to your existing data operations system, making it simple to incorporate outlier identification into your data processing and evaluation pipeline.