Learn MLOps with MLflow and Databricks – Full Course for Machine Learning Engineers

This end-to-end people provides a heavy dive into MLflow, the manufacture modular for managing the instrumentality learning life rhythm from section experimentation to production-ready deployment. You will maestro basal MLOps and LLM ops workflows, including research tracking, exemplary versioning, punctual management, and systematic information utilizing civilization scorers. Finally, the guideline demonstrates master integration pinch Databricks and Hugging Face to build reproducible, scalable, and observable ML systems for real-world endeavor environments. ✏️ Course from @datageekrj ❤️ Support for this transmission comes from our friends astatine Scrimba – the coding level that's reinvented interactive learning: https://scrimba.com/freecodecamp Contents Part 1: The Theory & Need for MLOps** 00:00 Introduction to MLflow and the Machine Learning Lifecycle 02:22 Why ML Systems Need Experiment Tracking 03:31 The Problem pinch Jupyter Notebook Scaling 06:22 Probabilistic vs. Deterministic Software Development 07:14 The 5 Core Components of an ML Experiment 10:20 Risks of Operating Without Tracking: Reproducibility and Audits Part 2: Local MLflow Implementation** 14:32 Local Setup and Virtual Environment Configuration 17:36 Installing MLflow and Starting the Tracking Server 21:14 Creating Your First Experiment and Logging Runs 24:44 Backend Store vs. Artifact Store: Understanding Where Data Lives 31:05 Technical Deep Dive: Exploring the MLflow SQLite Database 37:07 Comprehensive Logging: Parameters, Metrics, and Artifacts Part 3: Advanced Model Management** 44:43 Logging Media: Visualizing Loss Graphs and Images 48:28 Data Previews: Logging Pandas Tables and Data Frames 52:46 Training Models: Manual vs. Auto Logging pinch Scikit-Learn 59:01 The Model Registry: Lineage, Versioning, and Aliasing 01:13:36 Deployment Essentials: Understanding Model URIs 01:15:19 Serving Models arsenic Production HTTP Endpoints Part 4: LLM Ops & Prompt Engineering** 01:22:42 Introduction to GenAI Ops and managing LLM Prompts 01:25:34 The Prompt Registry: Building and Versioning Templates 01:28:25 Quality Control: Comparing Different Prompt Versions 01:37:43 Integrating MLflow Prompts pinch the OpenAI API 01:46:14 Systematic Prompt Evaluation Frameworks Part 5: Advanced LLM Evaluation** 01:54:39 LLM-as-a-Judge: Correctness and Guideline Scorers 02:00:11 Debugging Results: Understanding AI-Generated Rationales 02:09:00 Coding Custom Scorers for Specific Business Logic 02:13:54 Performance Visualization: Pass/Fail Trends and Comparative Runs Part 6: Databricks & Enterprise MLOps** 02:33:44 MLflow successful the Enterprise: The Databricks Advantage 02:39:27 Configuring Enterprise Compute and Serverless Clusters 02:51:12 Collaboration: User Management and the Unity Catalog 03:02:57 Registering and Serving Models successful Enterprise Environments 03:22:15 Real-world Case Study: Hugging Face Transformer Deployment Part 7: Databricks & Enterprise MLOps 03:38:20 MLflow successful the Enterprise: The Databricks Advantage 03:40:00 Setting Up a Databricks Account and Workspace 03:42:30 Configuring Serverless Compute and GPU Clusters 03:46:15 Workspace Notebooks and AI Coding Assistants 03:51:10 Enterprise Collaboration: User Management and Access Identity 04:12:50 Automated Experiment Tracking connected Databricks 04:18:20 Implementing Nested Runs for Sub-Hypothesis Testing 04:23:00 The Unity Catalog: Managing Models and Schemas 04:31:40 Registering Models into a Centralized Enterprise Registry 04:34:30 Real-time Model Serving connected Databricks 04:41:20 Securing Endpoints pinch Authentication Tokens Part 8: Advanced Project — Transformer Model Deployment 04:44:40 Real-World Case Study: Deploying Hugging Face Transformers 04:47:45 Environment Setup: Installing PyTorch and Transformers 04:50:40 Downloading and Localizing Embedding Models from Hugging Face 05:00:10 Building a Custom PyFunc Wrapper for Transformer Models 05:04:00 Implementing the Load Context and Predict Logic 05:17:20 Model Versioning and Registration successful Unity Catalog 05:21:15 Scaling Production Endpoints and Cold-Start Latency 05:27:15 Final Summary and Industry Workflow Conclusions 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news