Deepchecks Features

Utilizes artificial intelligence to analyze big data.

Utilizes machine learning to analyze big data.

Monitors logs and activities from a wide range of IT systems.

Directly identifies, or increases identification speed for, root causes for IT system issues.

Proactively identifies trends on IT systems that could lead to failures or errors.

Provides paths, suggestions, or other general assistance towards issue resolution.

Integrates with a variety of IT systems.

Automatically alerts necessary parties via email, text, or call when issues are identified.

Generate sreports and dashboards highlighting trends and key metrics around issues and issue resolution.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

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.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives