Udemy - MLflow for MLOps and LLMOps - Master MLflow with Databric...

MLflow for MLOps & LLMOps: Master MLflow with Databricks

https://WebToolTip.com

Published 4/2026
Created by Rahul Jha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 29 Lectures ( 6h 28m ) | Size: 3 GB

Learn MLflow for experiment tracking, model registry, model deployment, prompt management, and Databricks AI Functions

What you'll learn
✓ Understand how MLflow works internally and how it fits into real MLOps workflows for experiment tracking, model lifecycle management, and deployment.
✓ Track machine learning experiments using MLflow by logging parameters, metrics, artifacts, and runs in a structured and reproducible way.
✓ Build and manage ML models using MLflow Model Registry including versioning, lineage tracking, and production model management.
✓ Deploy ML models as REST APIs using MLflow’s built-in model serving capabilities for real-time inference.
✓ Implement LLMOps workflows using MLflow including prompt registry, prompt versioning, evaluation, and prompt management.
✓ Integrate MLflow with Databricks to manage machine learning experiments and production ML pipelines.
✓ Use Databricks AI Functions to perform tasks like sentiment analysis, classification, text extraction, and schema extraction using SQL.
✓ Build an end-to-end ML workflow including experiment tracking, model logging, model registry, and deployment.

Requirements
● Basic understanding of Python programming
● Familiarity with machine learning concepts such as models, datasets, and training
● A computer capable of running Python and Jupyter / VS Code
● A free Databricks account (we will show how to set it up)
● Curiosity to understand how real MLOps and LLMOps systems work in production