Accelerate AI/ML Model Development

Seamless access to all of the data you need for AI/ML

AI model development and training require access to vast quantities of data from across the business. Dremio breaks down data silos by eliminating the need for complex data integration through federated data access to all your data, whether on-premises, in the cloud, or across clouds. Dremio’s broad and growing connector ecosystem makes it easy to access all of your data where it lives.

Easily discover and prepare data for AI model training

Dremio’s intuitive UI for Unified Analytics makes it easy to quickly build curated, business-relevant data views across all of your data for model training. Dremio simplifies data preparation and data transformation with intuitive SQL capabilities that make it easy to manage and improve data quality to meet ML algorithm requirements, like removing nulls or duplicates.

GenAI and user-generated Wikis and data tagging provide clear business context so data scientists can clearly understand and discover relevant data to begin feature engineering and model training.

Faster, risk-free experimentation for AI

Machine learning models rely on a series of experiments to achieve strong results. Dremio enables rapid, risk-free experimentation with virtual data versioning that isolates experimental data branches from production datasets. Dremio Lakehouse Management, built on top of the open source Project Nessie, delivers Data as Code - Git-like branching that lets you perform various data-specific tasks in isolation without impacting production workloads, eliminating the need to create and manage additional dataset copies.

Quickly create virtual data branches with no data movement and conduct experiments against the virtual branch in Dremio.Git-like branching makes it easy, fast, and risk-free to scale experimentation.

Experiment using familiar tools and engines, including Notebooks and Spark

Dremio tightly integrates with Jupyter Notebooks, so you can access and analyze data using familiar environments that encourage experimentation and innovation. And, because Dremio is built using open standards, data scientists can also use their preferred engines, like Apache Spark.


An intuitive UI lets users author SQL or use drag-and-drop and GenAI text-to-SQL to write SQL to create data views, dashboards, and more. Together, these Dremio capabilities let you leverage powerful ML functions in conjunction with SQL to quickly iterate on experiments and features that drive AI/ML.

Open source framework easily plugs into solutions across your AI/ML infrastructure

Organizations use an array of tools across the AI lifecycle for model training, development, and operations. Dremio is foundationally built on open source technologies using open standards , including Arrow Flight. Once a model is deployed, Dremio allows you to quickly plug into best-of-breed ML Ops, observability, and graphing tools, like Dataiku and Data Robot, to manage your ML models effectively.

Time-travel and tagging for easy model reproducibility

Model reproducibility is critical to successful AI/ML. Dremio makes it easier to replicate AI/ML results with advanced dataset tagging and versioning that lets you instantly view historical data with no data copies or snapshots. Dremio’s virtual data versioning eliminates the cost, time, and governance risk created by managing data copies, and simplifies the typically time and cost intensive process of reproducing ML datasets.