MLOps

Official references for this objective

• Databricks — How Databricks Apps Facilitated Model Retraining | Databricks

  a fully-fledged computer vision labeling application could be built using apps, saving our retread SMEs months of manual effort and thousands of dollars in a

• Databricks — Dr. Jekyll and Mr. H-AI-de: Using MLflow and AI Judges to measure model alignment and safety | Databricks

  Deploy judges in MLflow to automate the evaluation of frontier open-source models.- Benchmark against emerging standards like HarmBench to quantify operational risk.

• Databricks — Feature and ML Platform Powered by Databricks | Databricks

  Key technologies highlighted include the DBX Feature Engineering SDK, Apache Spark Structured Streaming and Databricks Lakeflow jobs

• Databricks — Scaling Custom LLMs with vLLM and Databricks Model Serving: Fast, Flexible, and Production-Ready | Databricks

  packaging models, configuring vLLM runtime, and deployment! Serving LLMs on GPUs doesn’t have to be scary.

• Databricks — Accelerating AI From Model to Impact at Workday With Databricks Apps | Databricks

  SQL Serverless—enabling conversational experiences that let business users self‑serve sentiment analysis, forecasting and KPI insights without waiting on the data science team

• Databricks — Running LLaMA at Scale: Production Inference on Databricks Model Serving | Databricks

  unit‑economics tradeoffs that informed our ramp from pilot to majority traffic

• Databricks — Sponsored by: Deloitte | Databricks for Banking ML: Accelerating Delivery with an AI-Enabled Platform | Databricks

  leveraging its Brickbuilder agentic AI–enabled asset, “Data Assist,” to reverse engineer legacy platform and forward engineer into Databricks