Learn systematically all the knowledge needed for operating machine learning systems, from basic MLOps concepts to experiment management, pipeline automation, model management, and CI/CD
Series Overview
This series is a comprehensive 5-chapter practical educational content that allows you to learn MLOps (Machine Learning Operations) theory and implementation progressively from the basics.
MLOps (Machine Learning Operations) is a practical methodology for streamlining and automating the entire lifecycle from machine learning model development to production deployment, operations, and monitoring. Hyperparameter tracking through experiment management, data version control, centralized artifact management through model registries, workflow efficiency through pipeline automation of training, evaluation, and deployment, quality assurance and continuous delivery through CI/CD, and performance tracking in production environments through monitoring—these technologies have become essential skills for machine learning projects of all scales, from startups to large enterprises. You will understand and be able to implement productivity improvement technologies for machine learning that companies like Google, Netflix, and Uber have put into practical use. This series provides practical knowledge using major tools such as MLflow, Kubeflow, and Airflow.
Features:
- ✅ From Theory to Practice: Systematic learning from MLOps concepts to implementation and operations
- ✅ Implementation-Focused: Over 40 executable Python/MLflow/Kubeflow/Airflow code examples
- ✅ Practical Orientation: Practical workflows designed for real production environments
- ✅ Latest Technology Standards: Implementation using MLflow, Kubeflow, Airflow, and GitHub Actions
- ✅ Practical Applications: Hands-on experience with experiment management, pipeline automation, model management, and CI/CD
Total Learning Time: 5-6 hours (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (No MLOps knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Duration: 5-6 hours
For Intermediate Learners (With ML development experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 4-5 hours
For Specific Topic Enhancement:
- MLOps Fundamentals & ML Lifecycle: Chapter 1 (Focused learning)
- Experiment Management & DVC: Chapter 2 (Focused learning)
- Pipeline Automation: Chapter 3 (Focused learning)
- Model Management: Chapter 4 (Focused learning)
- CI/CD: Chapter 5 (Focused learning)
- Duration: 60-80 minutes/chapter
Chapter Details
Chapter 1: MLOps Fundamentals
Difficulty: Intermediate
Reading Time: 60-70 minutes
Code Examples: 6
Learning Contents
- What is MLOps - Definition, differences from DevOps, necessity
- ML Lifecycle - Data collection, training, evaluation, deployment, monitoring
- MLOps Challenges - Reproducibility, scalability, monitoring
- MLOps Tool Stack - MLflow, Kubeflow, Airflow, DVC
- MLOps Maturity Model - From Level 0 (manual) to Level 3 (automated)
Learning Objectives
- ✅ Understand basic MLOps concepts
- ✅ Explain each phase of the ML lifecycle
- ✅ Identify major MLOps challenges
- ✅ Understand the roles of major MLOps tools
- ✅ Explain the MLOps maturity model
Chapter 2: Experiment Management and Version Control
Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 10
Learning Contents
- Importance of Experiment Management - Hyperparameter tracking, metrics recording
- MLflow - Experiment tracking, model registry, project management
- Weights & Biases - Experiment visualization, team collaboration
- DVC (Data Version Control) - Data version control, pipeline definition
- Experiment Reproducibility - Seed fixing, environment management, dependency management
Learning Objectives
- ✅ Understand the importance of experiment management
- ✅ Track experiments with MLflow
- ✅ Version control data with DVC
- ✅ Ensure experiment reproducibility
- ✅ Manage hyperparameter tuning
Chapter 3: Pipeline Automation
Difficulty: Intermediate to Advanced
Reading Time: 70-80 minutes
Code Examples: 9
Learning Contents
- ML Pipeline Design - Data preprocessing, feature engineering, training, evaluation
- Apache Airflow - DAG definition, scheduling, dependency management
- Kubeflow Pipelines - Container-based pipelines, Kubernetes integration
- Prefect - Dynamic workflows, error handling, retries
- Workflow Design Patterns - Parallel execution, conditional branching, error handling
Learning Objectives
- ✅ Understand ML pipeline design principles
- ✅ Define DAGs with Airflow
- ✅ Create pipelines with Kubeflow
- ✅ Manage pipeline dependencies
- ✅ Implement error handling and retries
Chapter 4: Model Management
Difficulty: Intermediate to Advanced
Reading Time: 60-70 minutes
Code Examples: 8
Learning Contents
- Model Registry - Centralized model management, versioning, stage management
- Model Versioning - Semantic versioning, tag management
- Metadata Management - Model attributes, training conditions, evaluation metrics
- Model Deployment - Staging, Production, Archived
- A/B Testing - Canary release, shadow mode, gradual rollout
Learning Objectives
- ✅ Understand the role of model registries
- ✅ Implement model version control
- ✅ Properly manage metadata
- ✅ Implement model stage management
- ✅ Design A/B testing and canary releases
Chapter 5: CI/CD for ML
Difficulty: Advanced
Reading Time: 70-80 minutes
Code Examples: 9
Learning Contents
- CI/CD for ML - Data testing, model testing, integration testing
- GitHub Actions - Workflow definition, automation triggers, matrix builds
- Jenkins for ML - Pipeline construction, GPU environment management
- Automated Testing - Data validation, model performance testing, regression testing
- Deployment Strategies - Blue/green deployment, canary release, rollback
Learning Objectives
- ✅ Understand characteristics of ML-specific CI/CD
- ✅ Create workflows with GitHub Actions
- ✅ Implement automated data and model testing
- ✅ Design continuous deployment
- ✅ Select appropriate deployment strategies
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain basic MLOps concepts and the ML lifecycle
- ✅ Understand the importance of experiment management, pipeline automation, and model management
- ✅ Explain the roles and use cases of MLflow, Kubeflow, and Airflow
- ✅ Understand characteristics and challenges of ML-specific CI/CD
- ✅ Explain deployment strategies and A/B testing
Practical Skills (Doing)
- ✅ Track and manage experiments with MLflow
- ✅ Version control data and models with DVC
- ✅ Build ML pipelines with Airflow or Kubeflow
- ✅ Manage models using model registries
- ✅ Create ML-specific CI/CD pipelines with GitHub Actions
Application Ability (Applying)
- ✅ Select appropriate MLOps tools for projects
- ✅ Design and implement ML pipelines
- ✅ Ensure experiment reproducibility
- ✅ Design model deployment strategies
- ✅ Achieve quality assurance and continuous improvement of ML systems
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Fundamentals: Variables, functions, classes, modules
- ✅ Machine Learning Basics: Concepts of training, evaluation, and testing
- ✅ Command Line Operations: bash, basic terminal operations
- ✅ Git Basics: Commit, push, pull, branches
- ✅ Docker Basics: Containers, images, Dockerfile (Recommended)
Recommended (Nice to Have)
- 💡 Kubernetes Basics: Pod, Service, Deployment (when using Kubeflow)
- 💡 CI/CD Experience: GitHub Actions, Jenkins (for Chapter 5)
- 💡 Cloud Fundamentals: AWS, GCP, Azure (for deployment)
- 💡 scikit-learn/PyTorch: Model training implementation experience
- 💡 SQL Basics: For data management
Recommended Prior Learning:
- 📚 - ML fundamentals - REST API, Docker, Kubernetes
- 🎯 Feature Store (Coming Soon) (Coming Soon) - Feast, Tecton
Practical Projects
- 🚀 End-to-End ML Pipeline - Automation from data collection to deployment
- 🚀 A/B Testing Infrastructure - Model comparison and canary release
- 🚀 Real-time Inference System - Building low-latency inference APIs
- 🚀 Model Monitoring Dashboard - Performance visualization and anomaly detection
Update History
- 2025-10-21: v1.0 Initial release
Your MLOps journey starts here!