Collaboration across the full data and ML lifecycle
The Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle.
Centralized access to all of your data makes it simpler for data scientists to discover new insights or reuse features in a secure and governed manner.
Increase productivity by providing choice of best-in-class open source tools in a collaborative, reproducible, and managed environment.
Simplified devops/MLOps shortens time from experimentation to robust production deployments of data science and ML assets.
Quickly explore data with point-and-click visualizations or in the languages of your choice, collaborate, and share insights with stakeholders via live interactive dashboards.
Collaboratively build and manage models from experimentation to production, deploy for batch or real-time inference at scale, and monitor production workloads.
Discover insights on large data sets using SQL queries, built-in visualizations or dashboards, and connect to popular BI tools like PowerBI and Tableau.
Build robust data pipelines, automate and monitor production jobs using Scala, Java and built-in notebooks and APIs.
Use interactive notebooks with multi-language support to write commands in R, Python, Scala, or SQL and reuse your favorite Python, Java, or Scala libraries to quickly find insights.
Leverage a wide assortment of interactive point-and-click visualizations or use powerful scriptable options like matplotlib, ggplot, and D3 to see results.
Work on the same notebooks in real-time using your favorite tools and languages while automatically tracking changes and versions.
Share insights with your colleagues and customers, or let them run interactive queries with built-in dashboards.
Build state-of-the-art models with the most popular ML frameworks and augmented machine learning, from data preparation to inference.
Manage machine learning models from a centralized repository, seamlessly deploy to Databricks, containers, or inference services, and monitor performance.
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 with stakeholders.
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.
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.