Data Science et Machine Learning

Collaboration across the entire data and ML lifecycle

A collaborative, data-native platform for data science and machine learning that empowers data science and ML teams to prepare and process data in a self-service manner — and manage the full ML lifecycle from experimentation to production.

Increase the productivity of data teams

Accelerate collaboration — from data engineering to exploratory data analysis to production ML — with industry-standard tools on a unified, secure and scalable platform.

Simplify all aspects of data for data science and ML

Because we’re a data-native platform, we empower data science and machine learning teams to access, prepare and explore data at scale. Turn features into production pipelines in a self-service manner without waiting for data engineering.

Automate the full machine learning lifecycle

With automated handoffs and MLOps across the full lifecycle, you can accelerate innovation and shorten time from experimentation with machine learning models to robust production deployments.

With one line of code: mlflow.autolog(), you can:

Product components

Notebooks collaboratifs

Databricks notebooks natively support Python, R, SQL and Scala so practitioners can work together with the languages and libraries of their choice to discover, visualize and share insights. Learn More

Runtime de machine learning

One-click access to preconfigured ML clusters, powered by a scalable and reliable distribution of the most popular ML frameworks, with built-in AutoML and optimizations for unmatched performance at scale. Learn More

Mlflow administré

Built on top of MLflow — an open source platform from Databricks — Managed MLflow helps manage ML models from experimentation to production, with enterprise security, reliability and scale. Learn More