Tyler Faulkner, Databricks Certified Professional, on Tuesday, May 20 in an exclusive session shared valuable insights on streamlining and scaling machine learning workflows using the latest tools and best practices from Databricks.
Whether data scientists, ML engineers, or AI enthusiasts, participants walked away with actionable knowledge to build faster, smarter, and more reliable ML models.
Whether data scientists, ML engineers, or AI enthusiasts, participants walked away with actionable knowledge to build faster, smarter, and more reliable ML models.
What Attendees Learned:
- Experiment Tracking with MLflow – How to log, compare, and manage experiments at scale.
- Effortless Model Serving – Ways to deploy models via REST or SQL endpoints with minimal setup.
- Drift Detection with Lakehouse Monitoring – Techniques for staying ahead of performance issues with real-time monitoring.
- Unified Feature Store – How to serve consistent and reusable features across models.
Attendees participated virtually or in person.
Event Recap:
https://youtu.be/R62fWQUikGA?si=BqeM2SwMyZR8x1JU
Thanks to everyone who joined us for this informative and engaging session!
