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

Why It Matters

MLOps expertise is essential for enterprises to ensure reproducibility, governance, and rapid AI deployment, and this course provides the practical skills needed to achieve those goals.

Key Takeaways

• MLflow standardizes experiment tracking across ML lifecycle
• Course covers both traditional models and LLM prompt management
• Integration with Databricks enables scalable, serverless model serving
• Hands‑on projects include Hugging Face transformer deployment
• Emphasizes reproducibility, auditability, and enterprise collaboration

Pulse Analysis

The rapid expansion of machine‑learning initiatives has turned MLOps from a niche practice into a business imperative. Organizations seek tools that can capture experiments, version models, and guarantee reproducibility at scale. MLflow, now a CNCF‑graduated project, provides a unified interface for tracking parameters, metrics, artifacts, and model registries, bridging the gap between prototype notebooks and production pipelines. By mastering MLflow, engineers can enforce governance, simplify audits, and accelerate the transition from research to deployment, a capability that directly influences time‑to‑market for AI products.

Beyond traditional models, the surge of generative AI has introduced new operational challenges, such as prompt versioning and systematic evaluation of large language models. The course extends MLflow’s core functionality to LLM‑Ops, teaching prompt registries, custom scorers, and integration with the OpenAI API. Coupled with Databricks’ managed MLflow service, learners gain hands‑on experience configuring server‑less clusters, leveraging the Unity Catalog for model governance, and deploying Hugging Face transformers as secure HTTP endpoints. This enterprise‑grade workflow demonstrates how scalable compute and collaborative notebooks can support both batch and real‑time inference.

Delivering a complete, project‑based curriculum, the program equips machine‑learning engineers with immediately applicable skills. Participants build a transformer‑based service from scratch, implement custom PyFunc wrappers, and practice nested runs for hypothesis testing, mirroring real‑world production cycles. Such depth reduces onboarding time for AI teams and positions professionals to meet the growing demand for certified MLOps expertise. As more firms adopt AI at scale, structured training that combines open‑source tooling with cloud‑native platforms will become a differentiator in talent acquisition and competitive advantage.

Original Description

This end-to-end course provides a deep dive into MLflow, the industry standard for managing the machine learning life cycle from local experimentation to production-ready deployment. You will master essential MLOps and LLM ops workflows, including experiment tracking, model versioning, prompt management, and systematic evaluation using custom scorers. Finally, the guide demonstrates professional integration with Databricks and Hugging Face to build reproducible, scalable, and observable ML systems for real-world enterprise environments.

✏️ Course from @datageekrj

❤️ Support for this channel comes from our friends at Scrimba – the coding platform 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 with 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 with Scikit-Learn

59:01 The Model Registry: Lineage, Versioning, and Aliasing

01:13:36 Deployment Essentials: Understanding Model URIs

01:15:19 Serving Models as 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 with 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 in 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 in Enterprise Environments

03:22:15 Real-world Case Study: Hugging Face Transformer Deployment

Part 7: Databricks & Enterprise MLOps

03:38:20 MLflow in 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 on 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 on Databricks

04:41:20 Securing Endpoints with 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 in Unity Catalog

05:21:15 Scaling Production Endpoints and Cold-Start Latency

05:27:15 Final Summary and Industry Workflow Conclusions

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