Encord

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Transforms raw data into features that better represent the underlying problem to the predictive models

Offers image recognition services

Offers natural language processing services

Offers natural language generation services

Offers artificial neural networks for users

Offers image recognition services

Offers natural language understanding services

Offers natural language generation services

Provides deep learning capabilities

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Can integrate well with other software.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Allows users to manage model artifacts and tracks which models are deployed in production.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Gives user ability to import a variety of data sources for immediate use

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. 30 reviewers of Encord have provided feedback on this feature.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more. This feature was mentioned in 29 Encord reviews.

Ensures the data is of a high quality as compared to benchmark. 30 reviewers of Encord have provided feedback on this feature.

Gives user the ability to review and edit labels. 28 reviewers of Encord have provided feedback on this feature.

Based on 24 Encord reviews. Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

Automatically route input to the optimal labeler or labeling service based on predicted speed and cost. 23 reviewers of Encord have provided feedback on this feature.

Has the ability to place imaginary boxes or polygons around objects or pixels in an image. This feature was mentioned in 29 Encord reviews.

Based on 26 Encord reviews. has the ability to detect objects within images.

As reported in 22 Encord reviews. Track unique object IDs across multiple video frames

Based on 23 Encord reviews. Supports a range of different types of images (satelite, thermal cameras, etc.)

As reported in 15 Encord reviews. Gives user the ability to label and verify text data in an image.

Allows the user to transcribe audio. 13 reviewers of Encord have provided feedback on this feature.

Gives user the ability to label emotions in recorded audio. 12 reviewers of Encord have provided feedback on this feature.

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

Provides the ability to recognize and detect emotions.

Provides the ability to recognize various types of objects in various scenarios and settings.

Provides the ability to recognize texts.

Processes video, or image sequences, to track objects or individuals.

Allows users to detect logos in images.

Detects inappropriate material in images.

Provides the ability to detect objects, humans, etc. in video footage.

Allow users to analyze face attributes, such as whether or not the face is smiling or the eyes are open.

Give users the ability to compare different faces to one another.

Allows users to train model and provide feedback regarding the model's outputs.

Allows users to select given items in an image for the purposes of image recognition.

Provides the ability to build custom image detection models.

Enables smart selection of data for annotation to reduce overall training time and costs.

Allows for automatic retraining of models with newly annotated data for continuous improvement.

Facilitates the setup of an active learning process tailored to specific AI projects.

Allows users to establish a feedback loop between data annotation and model training.

Provides the ability to identify and address edge cases to enhance model robustness.

Enables efficient triaging of training data to identify which data points should be labeled next.

Streamlines the data labeling process with tools designed for efficiency and accuracy.

Automates the detection of anomalies and outliers in the training data for correction.

Offers tools to optimize the selection of data for labeling based on model uncertainty.

Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Delivers in-depth insights into factors impacting model performance and suggests enhancements.

Enables model improvement at the lowest possible cost by focusing on the most impactful data.

Integrates the handling of edge cases into the model training loop for continuous performance enhancement.

Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.

Offers advanced tools to analyze label outliers and errors to inform further model training.

Supports integration with multiple data sources for seamless data input.

Enhances output accuracy and speed through efficient ingestion and processing of training data.

Generates actionable insights by applying learned patterns to key issues.

Continuously improves and adapts to new data using specified algorithms.