Encord Active is an advanced active learning toolkit designed to enhance the process of building AI models. It offers various features to test, validate, and evaluate models, as well as surface, curate, and prioritize valuable data for labeling, ultimately improving model performance.One of its key features is the ability to automatically find label errors in training data without manual inspection.

By utilizing vector embeddings, AI-assisted quality metrics, and model predictions, Encord Active identifies problematic data samples and enables course correction.Additionally, the tool introduces a unique approach to data search using natural language search.

With Active, users can search and curate visual data, including images, videos, DICOM files, labels, and metadata, using only natural language.Encord Active also allows users to debug models and enhance performance by identifying and fixing dataset errors, biases, and edge cases.

It conducts model error analysis, runs automated robustness tests, and provides explainability reports to help uncover failure modes and issues.Furthermore, the platform provides out-of-the-box metrics or the option to integrate custom metrics for detailed breakdowns of how data and labels impact models.

Versioning and comparison features enable users to track progress by comparing datasets and models.The tool further supports the creation of Active Learning pipelines that combine acquisition functions, data distribution, model confidence, and similarity search to curate high-value data that improves model performance.Encord Active also offers integrations with secure cloud storage, MLOps tools, and other components of users' ML pipelines, ensuring seamless workflow integration.Overall, Encord Active is a comprehensive active learning platform that streamlines data flows, facilitates collaboration, and empowers AI teams to build reliable models with improved efficiency.

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Encord was manually vetted by our editorial team and was first featured on July 21st 2023.

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Pros and Cons

Pros

Advanced active learning toolkit

Automatic label error detection

Natural language search for data

Debugging and performance enhancement capabilities

Detailed dataset impact breakdown

Customizable metrics integration

Versioning and comparison features

Creates Active Learning pipelines

Seamless workflow integration

Comprehensive active learning platform

Cloud storage integration

Integration with MLOps tools

Model explainability reports

Automated robustness tests

Supports visual data search

Prioritize data for labeling

Model error analysis

Secure platform

SOC2, HIPAA, and GDPR compliant

Pre-built integrations with AWS, Azure, Google Cloud

API & SDK for programmatic access

Cons

Limited data types supported

No mobile application

Potential complexity in setup

Might require technical skillset

Limited pre-built integrations

No offline functionality

Lack of transparent pricing

Unclear version control system

Unknown database compatibility

Language limitations for non-English

Q&A

What is Encord Active?

Encord Active is an advanced learning toolkit whose design aims at enhancing the process of building AI models. It serves several key functions, such as testing, validating, evaluating models, surfacing, curating, and prioritizing valuable data for labeling. It assists in improving model performance and aids in the automatic detection of label errors in training data.

What functionalities does Encord Active offer to enhance AI model building?

Encord Active offers various features to refine AI model building. It aids in finding label errors in training data by using vector embeddings, AI-assisted quality metrics, and model predictions. It assists in data curation and prioritization through a natural language search feature. It also enables the debugging of models by identifying and rectifying dataset errors, biases, and edge cases, conducting model error analysis, and running automated robustness tests. Moreover, Encord Active provides out-of-the-box metrics or custom metric integration, and facilitates versioning and comparison of datasets and models.

How does Encord Active automate the process of finding label errors in training data?

Encord Active employs automation to identify label errors in training data without the need for manual inspection. This capability stems from the use of vector embeddings, AI-assisted quality metrics, and model predictions to locate problematic data samples, leading to strategic course correction.

What is the role of vector embeddings, AI-assisted quality metrics, and model predictions in Encord Active?

Vector embeddings, AI-assisted quality metrics, and model predictions play a crucial role in Encord Active. These technologies aid in automatically locating label errors in training data. Vector embeddings are representations of data points in a mathematical space, which Encord Active uses to correlate data. AI-assisted quality metrics deliver intelligent measures of overall model performance. Model predictions provide insights into potential outcomes based on the trained data, which aids in identifying problematic data samples.

How does the natural language data search work in Encord Active?

Encord Active utilizes a unique approach to data search by employing natural language. Users can search and curate visual data, including images, videos, DICOM files, labels, and metadata, using only natural language, significantly simplifying the process of data navigation.

What visual data types can I search with Encord Active's natural language search?

The visual data types that can be searched using Encord Active's natural language search include images, videos, DICOM files, labels, and metadata.

How does Encord Active identify and resolve dataset errors and biases?

Encord Active identifies and resolves dataset errors and biases by conducting model error analysis and automated robustness tests. This process allows the uncovering of model failure modes and issues which can then be rectified in a timely manner.

What type of reports does Encord Active provide for understanding failure modes and issues in models?

Encord Active provides explainability reports for understanding failure modes and issues in models. These reports deliver detailed insights into model errors, biases, edge cases and may facilitate correcting them.

Does Encord Active offer customizable metrics for model evaluation?

Yes, Encord Active offers customizable metrics for model evaluation. Users can use out-of-the-box metrics or integrate their own custom metrics for detailed breakdowns of how data and labels impact their models.

How does Encord Active support versioning and comparison of datasets and models?

Encord Active supports versioning and comparison of datasets and models. This feature permits users to track progress by allowing them to compare different versions of datasets and models to analyze their evolution over time.

What are the Active Learning pipelines in Encord Active and how do they improve model performance?

Active Learning pipelines in Encord Active are mechanisms that incorporate acquisition functions, data distribution, model confidence, and similarity searching. These pipelines help curate high-value data that can enhance model performance by focusing on the most valuable and informative data samples for training AI models.

Which secure cloud storages and MLOps tools does Encord Active integrate with?

Encord Active integrates with secure cloud storage and MLOps tools. Although the specific storage providers and tools aren't explicitly mentioned, it emphasizes seamless workflow integration and dedicated integrations with the users' ML pipelines' components.

Does Encord Active facilitate maintaining regulatory compliance in AI model creation?

Yes, Encord Active facilitates maintaining regulatory compliance in AI model creation. It asserts staying on top of ever-increasing compliance and regulation needs, providing total visibility into every step of the model production process.

How is Encord Active different from other AI tools?

Comparative facts to other AI tools were not mentioned explicitly, however, customer testimonials suggest that Encord Active makes automatic calculations, provides more insightful metadata interpretation, and surfaces insights they had never found through other platforms.

Can Encord Active be integrated with my existing ML pipeline?

Yes, Encord Active can be integrated with an existing ML pipeline. It offers dedicated integrations that seamlessly slot into user workflows connecting secure cloud storage, MLOps tools, and other components of the ML pipeline.

What areas of AI model building does Encord Active streamline?

Encord Active streamlines several areas of AI model building. Specifically, it aids in the automatic detection and correction of label errors, data curation and prioritization, model debugging and performance enhancement, and conducting of model error analysis. It also facilitates the creation of Active Learning pipelines and comprehensive evaluation of models with customizable metrics.

How does Encord Active help in debugging models and boosting their performance?

Encord Active helps in debugging models and boosting their performance by finding and fixing dataset errors, biases, and edge cases. It conducts model error analysis and runs automated robustness tests to uncover failure modes and issues that can be rectified to enhance model performance.

What features of Encord Active assist in prioritizing the most valuable data for model learning?

Encord Active assists in prioritizing the most valuable data for model learning through features like natural language search and Active Learning pipelines. With these features, users can curate and prioritize valuable data, including images, videos, DICOM files, labels, and metadata. Active Learning pipelines take into account acquisition functions, data distribution, model confidence, and similarity searching to surface and prioritize high-value data for model performance improvement.

How does Encord Active support label and data management for model training?

Encord Active supports label and data management for model training by automating the process of finding label errors within training data, facilitating the curation and prioritizing of valuable data using natural language search, and creating Active Learning pipelines that curate high-value data for improved model performance.

Are there any major companies or AI teams who use Encord Active and what have been their experiences?

Major companies or AI teams using Encord Active were not explicitly mentioned on their website, however, customer testimonials assert a variety of benefits from using the platform. These include faster and more efficient realization of results, automatic computation and deeper insights compared to other platforms, and significant improvements in last-mile performance and edge-case class.