SuperAnnotate

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.

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.

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 a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. This feature was mentioned in 54 SuperAnnotate reviews.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more. This feature was mentioned in 52 SuperAnnotate reviews.

Ensures the data is of a high quality as compared to benchmark. This feature was mentioned in 55 SuperAnnotate reviews.

Gives user the ability to review and edit labels. This feature was mentioned in 47 SuperAnnotate reviews.

Based on 37 SuperAnnotate 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. 27 reviewers of SuperAnnotate 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 50 SuperAnnotate reviews.

has the ability to detect objects within images. 48 reviewers of SuperAnnotate have provided feedback on this feature.

Track unique object IDs across multiple video frames This feature was mentioned in 39 SuperAnnotate reviews.

Supports a range of different types of images (satelite, thermal cameras, etc.) 41 reviewers of SuperAnnotate have provided feedback on this feature.

As reported in 26 SuperAnnotate reviews. Gives user the ability to extract entities from text (such as locations and names).

Gives user the ability to tag text based on its sentiment. 19 reviewers of SuperAnnotate have provided feedback on this feature.

As reported in 23 SuperAnnotate reviews. Gives user the ability to label and verify text data in an image.

As reported in 20 SuperAnnotate reviews. Allows the user to transcribe audio.

Based on 19 SuperAnnotate reviews. Gives user the ability to label emotions in recorded audio.

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 users with the ability to test and optimize prompts to improve LLM output quality and efficiency.

Gives users a collection of reusable prompt templates for various LLM tasks to accelerate development and standardize output.

Offers tools for users to compare multiple LLMs side-by-side based on performance, speed, and accuracy metrics.

Provides users with a user-friendly interface for fine-tuning LLMs on their specific datasets, allowing better alignment with business needs.

Gives users tools to integrate LLM functionality into their existing applications through SDKs and APIs, simplifying development.

Offers users the capability to deploy models quickly to production environments with minimal effort and configuration.

Provides users with tools to automatically scale LLM resources based on demand, ensuring efficient usage and cost-effectiveness.

Gives users the ability to set boundaries and filters to prevent inappropriate or sensitive outputs from the LLM.

Offers users tools to ensure their LLMs adhere to compliance standards such as GDPR, HIPAA, and other regulations, reducing risk and liability.

Gives users notifications when the LLM performance deviates significantly from expected norms, indicating potential model drift or data issues.

Provides users with live insights into model accuracy, latency, and user interaction, helping them identify and address issues promptly.

Provides users with encryption capabilities for data in transit and at rest, ensuring secure communication and storage when working with LLMs.

Offers users tools to set access permissions for different roles, ensuring only authorized personnel can interact with or modify LLM resources.

Provides users with middleware to route requests efficiently to the appropriate LLM based on criteria like cost, performance, or specific use cases.

Gives users tools to process multiple inputs in parallel, improving inference speed and cost-effectiveness for high-demand scenarios.