Train and fine-tune models faster with AI Runtime

Here is the updated YouTube description with that phrasing revised: *Train and Fine-Tune Models Faster on Your Enterprise Data* As enterprises train larger models, legacy GPU infrastructure can slow developers down, complicate management, and make governance and security challenging. Databricks AI Runtime (AIR) brings a managed GPU stack directly into Databricks, providing a faster way to train and fine-tune AI models at scale. In this video, learn how AI Runtime helps you streamline GPU training, scale multi-node workloads using simple configuration files, track experiments in MLflow, and use Genie Code to automatically analyze and optimize training runs. ----- Key Pillars: - *Hassle-Free GPU Compute:* AI Runtime handles capacity through serverless, ensures GPU and RDMA reliability through health checks, and optimizes training and data loading to boost utilization. - *Experiment Faster:* GPUs spin up quickly, declarative CLIs enable rapid experimentation, and built-in observability and experiment tracking make debugging simple. - *Governed and Secure:* AI Runtime extends enterprise data controls and governance to GPU workloads, backed by serverless isolation and private networking. ----- Highlights in This Video: - *0:00* – Introduction to AI Runtime (AIR) on Databricks - *0:20* – Setting up training parameters and custom environments using `train.yaml` - *1:15* – Launching a multi-node fine-tuning job using the `air` CLI - *1:38* – Fine-tuning a 2B parameter model on 16 H100 GPUs - *2:00* – Monitoring metrics, GPU utilization, and memory footprint in MLflow - *2:22* – Using Genie Code's `Analyze Run` feature to get automated optimization recommendations - *3:04* – Summary