Gal Oshri

Genie Code is an autonomous AI partner for data work, with depth across the data engineering lifecycle. We show how builders use it to iterate on Spark Declarative Pipelines and Lakeflow Jobs, accelerating authoring. It knows your data, grounded in Unity Catalog metadata, semantics, and governance policies. This grounding helps Genie Code take accurate and context-aware actions. Beyond authoring, Genie Code keeps workflows healthy, proactively monitoring and maintaining data and AI in production. We discuss how it autonomously detects, diagnoses, and proposes fixes the moment failures happen, with every agent running sandboxed, so production stays untouched.

What if you could go from a raw dataset to a trained model in a single conversation? Data scientists spend a lot of time on the repetitive parts of the lifecycle: finding data, wrangling it, writing features, tuning hyperparameters, and not enough time on the creative work that actually moves the needle. Genie Code is an autonomous AI partner built into Databricks that changes the equation. Tell it what you want to explore, and it writes the code, runs the analysis, and iterates with you. In this session, we’ll walk through how to apply Genie Code across the data science lifecycle. We’ll cover: - How to use Genie Code for exploratory data analysis and feature discovery - How to build and iterate on machine learning models with less manual effort - When to steer and when to let Genie Code drive - Best practices for working effectively with Genie Code as a data science partner

Databricks Assistant helps you get from initial exploration all the way to production faster and easier than ever. In this session, we'll show you how Assistant simplifies and accelerates common workflows, boosting your productivity across notebooks and the SQL editor. You'll get practical tips, see end-to-end examples in action, and hear about the latest capabilities we're excited about. We'll also discuss how we're continually improving Assistant to make your development experience faster, more contextual and more customizable. Join us to discover how to get the most out of Databricks Assistant and empower your team to build better and faster.

Amazon SageMaker HyperPod accelerates FM training at scale for customers of all sizes and industries. In this chalk talk, we walk you through leveraging Amazon SageMaker HyperPod’s distributed training capabilities to reduce training time by up to 40%.

Train machine learning (ML) models faster on Amazon SageMaker with state-of-the-art training tools and the highest-performing ML compute infrastructure currently available. SageMaker interactive debugging and profiling tools can uncover complex model behaviors and dissect hardware utilization in near real time.

Training ML models at scale often requires significant investments. In this session, learn how Amazon SageMaker reduces the time and cost to train and tune large-scale ML models without the need to manage infrastructure.