Build, train, and run machine learning on Databricks
Move from fragmented ML stacks to one open, governed platform.
ML gets harder as it scales.
Separate tools for data, training, deployment, and monitoring create infrastructure toil, fragmented governance, and repeated handoffs that slow teams down and make every model harder to operate.
The Databricks approach:
Bring ML to your data, not your data to another ML platform
Build better models faster with agentic ML: Accelerate development and operations with Genie Code and Genie ZeroOps.
Scale across CPU and GPU compute: Run classic ML, deep learning, fine-tuning, and inference on managed infrastructure.
Run mission-critical ML in production: Support batch and real-time workloads with serving, monitoring, and MLOps.
Build, scale, and operate ML across the lifecycle
Work with an intelligent coding partner
Data science is iterative. Practitioners move constantly between understanding data, creating features, selecting approaches, evaluating results, and debugging what goes wrong.
Genie Code works alongside data scientists and ML engineers throughout that process. It can explore governed data, develop and refine code, build features and models, evaluate results, and troubleshoot workloads directly in Databricks.
With context from Genie Ontology, Genie Code can build on your team’s existing feature patterns, training workflows, and evaluation criteria instead of starting every task from scratch.
Build and train models on governed data
Move from feature development to model training without separating your ML stack from the data behind it.
Use Feature Store to create reusable batch and streaming features and generate point-in-time-correct training data. Train everything from classic ML to deep learning and custom AI models using the frameworks and techniques that fit the problem, with managed compute connected directly to governed enterprise data.
Track experiments and evaluate models with MLflow while keeping data, features, code, and model metadata connected throughout development.
Scale AI and ML on GPUs
Run GPU-intensive AI and ML workloads without managing GPU infrastructure. AI Runtime provides serverless GPU compute for deep learning, distributed training, model fine-tuning and customization, and GPU inference.
Scale from interactive development to multi-node workloads while keeping compute connected to governed enterprise data, MLflow, and the rest of the Databricks ML lifecycle.
Operationalize and run production ML
Production ML requires more than deploying a model. Teams need repeatable MLOps practices for moving changes into production, managing models and infrastructure, monitoring production systems, and responding when something goes wrong.
Databricks brings model development and MLOps workflows onto the same platform. Manage model versions and the production lifecycle with MLflow, define ML resources and deployment workflows as code with Declarative Automation Bundles, and integrate them into CI/CD.
Serve models for real-time inference, keep feature logic consistent from training to production, and monitor data, inference, and serving behavior over time. Genie ZeroOps adds agentic assistance to ML operations, helping teams investigate issues across models, serving infrastructure, and upstream pipelines and assist with remediation.
Build data, ML, and agents together
Machine learning does not operate in isolation. Data pipelines feed features and training, models power applications and agents, and production signals feed monitoring and retraining.
Databricks connects data engineering, machine learning, and agent development so the outputs of one workflow can become inputs to the next. Build pipelines that feed reusable features and training data, deploy models for applications and agents, and use production signals to continuously improve those systems.
Shared governance and lineage keep those workflows connected as they move across teams and production systems.
How it works
Data, features, experiments, models, inference, and production telemetry remain connected as ML moves from development into production. That shared context powers governance, automation, lineage, and agentic assistance across the lifecycle.
Databricks for Machine Learning
Explore the core capabilities for model development, training, MLOps, and production ML on Databricks.
Genie Code
Work with an agentic coding partner built for data science and machine learning. Genie Code helps explore governed data, build features, develop and evaluate models, reuse existing workflows, and troubleshoot issues across development and production.
Resources
Report
Blog
Blog
Ready to get started?