The AI kit for Data Scientists

Walks you through cleaning, imputation, scaling, and imbalance fixes, like turning a 40 percent missing column into a usable feature.

Suggests new features and transformations, such as lag and rolling averages for weekly sales forecasting.

Covers the research lifecycle from preprocessing to documentation, for example structuring a paper-ready model comparison.

Recommends and compares models for your data, like choosing gradient boosting over logistic regression for tabular churn.

Like "Image Classification Assistant"

Like "Active Learning Integration"

Like "Outlier Detection Methods Guide"

Like "Categorical Variable Encoding Guidance"

A validated, reproducible working dataset with raw data untouched.

A current register of live models with owners, reviews and risk status

A test result read only when the data is valid, with a clear recommendation

Forecasts that improve over time because every method is tested on the same history

Let your AI write and run code inside your Jupyter Notebook, cell by cell, and read the results.

Let your AI open, edit and run Jupyter notebooks for you while you watch.

Lets your AI look up MLflow experiments, runs, models, and traces and summarize them for you.

Let your AI run optimization experiments and show you the results as charts.

Evidently AI is an open-source framework for evaluating, testing, and monitoring AI applications with 100+ built-in checks, supporting offline and live assessme

Explore a vibrant AI community platform dedicated to crafting and deploying machine learning models. Tackle challenges in audio, vision, and language with cutti

Modal Notebooks offers a collaborative platform for high-performance interactive computing, enabling users to explore data, run Python code, and test ideas inst

Kolena streamlines the AI development process with automated testing and evaluation tools for machine learning models. Enhance model quality and explainability

Reduce notebook rework while preserving the analyst's reasoning.

Runs training and deployment in one place, cutting handoffs between notebooks and production.

Tests models before release and keeps a defensible record, useful for regulated or high-stakes deployments.

Fine-tunes models on your own data while keeping weights and data in-house.

Analyzes data, builds models, and delivers actionable business insights from complex datasets.

Runs statistical tests and reports results in APA format for academic research.

Build and evaluate classical ML models with scikit-learn pipelines.

Orchestrate a multi-agent MLOps pipeline from data ingestion to production serving.