Train and Finetune Models Without Managing GPUs: Introducing AI Runtime

Enterprise AI is entering a new era. Organizations are moving beyond small models to train and fine-tune increasingly sophisticated AI systems—from foundation models and recommendation engines to forecasting, computer vision, and reinforcement learning workloads. Yet scaling AI often means managing GPUs, distributed training, and complex infrastructure.

In this session, we'll introduce AI Runtime, Databricks' new serverless GPU training experience. Powered by on-demand GPUs and tightly integrated with Lakehouse data, AI Runtime gives data scientists and ML engineers instant access to high-performance compute for training and fine-tuning models at scale.

We'll cover the latest trends in deep learning, transformer architectures, and open-source foundation models, and demonstrate how AI Runtime works seamlessly with PyTorch, Hugging Face, and other leading frameworks. You'll also see how Databricks delivers a unified deep learning experience across MLflow, Lakeflow, Unity Catalog, Genie Code, and Notebooks.

Join us to learn how to accelerate experimentation, optimize GPU workloads, leverage coding agents, and move from idea to production faster with AI Runtime—with no infrastructure management required.