Tejas Sundaresan

Coding agents are transforming how data science teams build and operate machine learning systems. In this session, we'll explore how teams use Genie Code across the ML lifecycle—across EDA, feature engineering, model development, evaluation, deployment, and monitoring. We'll also provide an inside look at how Genie Code was built and optimized for ML workflows. Genie Code understands your data through Unity Catalog and leverages metadata, business semantics, governance policies, and lineage to provide context-aware assistance. Deep integration across the Databricks ML stack enables teams to move faster and work with greater confidence. We'll then look ahead at how Genie Code is evolving from a coding assistant into an agent that can help deploy, serve, and operate ML systems in production. You'll also learn how to customize Genie Code for your ML team through skills and instructions, as well as other tips and tricks to get the most out of Genie Code. Attendees will leave with practical examples of how coding agents are reshaping machine learning and actionable guidance for applying these capabilities to their own ML projects.

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…

The last year has seen the rapid progress of Open Source GenAI models and frameworks. This talk covers best practices for custom training and OSS GenAI finetuning on Databricks, powered by the newly announced Serverless GPU Compute. We’ll cover how to use Serverless GPU compute to power AI training/GenAI finetuning workloads and framework support for libraries like LLM Foundry, Composer, HuggingFace, and more. Lastly, we’ll cover how to leverage MLFlow and the Databricks Lakehouse to streamline the end to end development of these models. Key takeaways include: - How Serverless GPU compute saves customers valuable developer time and overhead when dealing with GPU infrastructure - Best practices for training custom deep learning models (forecasting, recommendation, personalization) and finetuning OSS GenAI Models on GPUs across the Databricks stack - Leveraging distributed GPU training frameworks (e.g. Pytorch, Huggingface) on Databricks - Streamlining the path to production for these models Join us to learn about the newly announced Serverless GPU Compute and the latest updates to GPU training and finetuning on Databricks!