Puneet Jain

Deep learning breaks most MLOps playbooks. Multi-gigabyte weights, GPU-heavy training, distributed checkpoints, and fine-tuning loops don't fit the lifecycle patterns built for classical ML. Teams end up stitching together fragile pipelines to move models from notebook to production — and paying for it in downtime, drift, and missed retraining cycles.This session shows how to run deep learning MLOps end-to-end on Databricks: distributed training with MLflow tracking, model registry workflows for large models, GPU-aware serving with traffic splitting and autoscaling, and automated monitoring that catches regressions before users do.You'll leave with a reference architecture for deep learning MLOps on Databricks — and the patterns to ship models that stay reliable long after launch.