What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML

What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML | Databricks Blog Skip to main content

Summary

Build ML systems faster with Genie Code, a coding agent that helps data scientists and ML engineers develop, evaluate, and improve traditional machine learning systems.

Train and fine-tune AI models on serverless GPUs with AI Runtime, a unified deep learning platform optimized for large-scale GPU training and experimentation.

*Power real-time ML at scale with new Feature Store and Model Serving capabilities, including streaming features and high-QPS serving for the most demanding production workloads.

There’s never been a more dynamic, exciting time to be building your own AI models and systems. From demand forecasting and fraud detection to search, recommendations, personalization, and multimodal AI, machine learning is powering critical applications across every industry. At Data + AI Summit 2026, we're thrilled to announce the following new capabilities within the Databricks AI Platform: Genie Code for ML: Genie Code now comes with upgraded intelligence for ML engineering and native integrations across every Databricks ML Platform component: feature engineering, model training, serving, and monitoring. AI Runtime (Public Preview): a serverless GPU training environment, enabling research-grade deep learning and fine-tuning without complex infrastructure management. Enhanced Support for Real-time ML: including low-latency, high QPS support across our Feature Store and Model Serving products.

Together, these capabilities streamline the path from experimentation to production, enabling organizations to build, deploy, and scale AI applications an significantly faster than ever before. Let's take a closer look at what's new. Genie Code for Machine Learning Today, bringing an ML model to production can take months, with teams spending countless hours on repetitive tasks across the ML lifecycle—from feature engineering and experiment management to model evaluation, and deployment. But agents have transformed how engineering and technical teams operate. To that end, at DAIS this year, we’re excited to announce Genie Code ’s support for the entire ML lifecycle. Building and operating ML models takes nuanced decisions that generic coding agents can't make. Can I rely on this dataset’s freshness and quality as a feature? Will this feature leak future information into the model? Is this serving endpoint starting to drift? Getting the details in ML right requires deep context, and that context only comes from tight integration with the data and ML platform: your data and its quality, feature lineage, experiment history, training infrastructure, and production performance. That's where Genie Code comes in: Context on your data via Unity Catalog: Genie Code understands your data, business semantics, and governance model. Through its integration with Unity Catalog, it knows which tables and features are high quality for ML, how data flows through your ML pipelines, and what access controls and policies must be respected. Context on the Databricks ML stack: Genie Code is built for ML on Databricks and integrates deeply with Feature Store, Serverless Compute, AI Runtime, Model Serving, and Inference Tables. It can optimize training jobs, diagnose serving issues, evaluate challenger models, and take action across the ML stack, not just generate code that interacts with it. Context on your ML lifecycle and workflows: Through MLflow, Genie Code understands the full ML lifecycle , from feature engineering and experimentation to deployment, monitoring, drift detection, retraining, and production operations. It doesn't stop when a model is shipped; it helps ensure the business metrics that model drives, such as CTR, conversion, or revenue, stay healthy in production.

And so, with Genie Code, your ML teams can move faster than ever before. Expand

Genie Code handles feature engineering the way your senior ML engineer would—learning your team's existing patterns, reusing proven transformations, and building features that are consistent with what's already in production. Genie Code doesn't just write ML code—it trains and tunes production-grade models. It automatically selects and configures the right infrastructure, whether that's CPU for lightweight experiments or GPU for distributed training, and logs every run natively to MLflow. Expand

Genie Code takes models from notebook to production in one flow—registering to Unity Catalog, deploying to a serving endpoint, and keeping governance intact every step of the way. Genie Code has completely changed how I work. I run upwards of 15 parallel threads scoped to different notebooks and assets every day, and managing all of that across tabs is one of the biggest sources of friction in my workflow. Full page Genie Code with concurrent sessions would give me a true workspace for running everything in parallel without constantly losing context. — Moritz Schiek, Solution Consultant, Bosch With Genie Code, we moved from raw data to a governed, production-ready ML workflow in 90 minutes. Because it uniquely understands production ML workflows on Databricks, it helped us create Delta tables, explore the data, train and compare models, register them with MLflow and Unity Catalog, and deploy the champion model to a serving endpoint, with time left to optimize for the business outcome that mattered most. — Radu Dragusin, Principal Engineer, Data & AI, Danfoss To learn more about Genie Code, please get started here ! Introducing AI Runtime: A Research Grade GPU Platform within the Lakehouse GPUs power today’s most advanced AI workloads—from forecasting and recommendations to multimodal foundation models. But deep learning teams struggle to procure and manage GPU infrastructure, configure distributed training environments, and resolve performance bottlenecks. They prefer to focus on modeling instead of infrastructure. In March, we launched a preview of AI Runtime, and today, we’re excited to share, as part of Data AI Summit, that AI Runtime now supports high performance multinode training. With AI Runtime , Databricks users now have: Serverless, on-demand NVIDIA GPUs: Simply configure your notebook in 2-3 clicks, and get fast attach to Serverless A10 and H100 GPUs to start training – no cluster needed. Only pay for the GPUs that you use,...