Machine Learning Operations (MLOps) help to bridge the gap between Data Science and Production.
Course Overview:
This comprehensive course provides a thorough understanding of MLOps principles, practices, and real-world applications. Participants will gain hands-on experience with the tools and techniques necessary to deploy, manage, and maintain machine learning models in production.
Target Audience:
- Data scientists
- Machine learning engineers
- Developers interested in machine learning
- Anyone involved in the development and deployment of machine learning models
Learning Objectives:
- Understand the fundamental concepts and principles of MLOps
- Identify the key challenges and considerations in deploying and maintaining ML models in production
- Learn how to design and implement data pipelines for ML
- Develop and evaluate machine learning models for production
- Deploy and monitor ML models using various methods and tools
- Understand the role of cloud computing and containerization in ML deployment
- Explore advanced MLOps practices, including CI/CD, experimentation, and monitoring
- Apply MLOps principles to real-world case studies and projects
- Discuss ethical considerations and the future of MLOps
Course Outline:
Module 1: Introduction to MLOps
Lesson 1: Defining MLOps and Its Importance
- Overview of MLOps and its role in machine learning
- Challenges of deploying and maintaining ML models in production
- Benefits of adopting MLops practices
Lesson 2: The ML Lifecycle and Components
- Different stages of the ML lifecycle
- Key components of an ML system (data pipelines, models, infrastructure)
- Importance of version control and reproducibility in ML
Module 2: Tools and Technologies for MLops
Lesson 3: Version Control for ML Models
- Git and Git LFS
- Model versioning tools (e.g., DVC)
Lesson 4: Containerization and Orchestration for ML
- Docker for packaging models
- Kubernetes for orchestration
Lesson 5: Continuous Integration/Continuous Delivery (CI/CD) for ML
- Setting up CI/CD pipelines
- Automated testing and validation
Module 3: Model Deployment and Monitoring
Lesson 6: Model Deployment Strategies
- A/B testing
- Blue-green deployments
- Canary releases
Lesson 7: Scalability and Performance Optimization
- Handling large-scale deployments
- Load balancing and auto-scaling
Lesson 8: Monitoring and Logging
- Implementing monitoring solutions
- Logging best practices
Module 4: Model Governance and Security
Lesson 9: Model Governance
- Model tracking and metadata
- Compliance and regulatory considerations
Lesson 10: Security Best Practices
- Data security
- Model security
Module 5: Model Maintenance and Retraining
Lesson 11: Model Maintenance
- Handling model drift
- Updating dependencies
Lesson 12: Continuous Learning and Retraining
- Incremental model training
- Retraining strategies
Module 6: Case Studies and Real-World Projects
Lesson 13: Industry Use Cases
- Case studies from various industries
- Lessons learned from real-world MLOps implementations
Capstone Project:
Participants work on a hands-on project applying MLOps principles to deploy and manage a machine learning model in a realistic scenario.
Ethical Considerations and the Future of MLops
- Ethical concerns and responsible AI practices in ML development
- Emerging trends and advancements in MLops
- Preparing for the future of machine learning and AI
Hands-on Projects and Practical Applications
- Opportunities to apply MLops practices through hands-on projects
- Exploration and experimentation with ML tools and frameworks
- Collaborative learning environment for sharing experiences and insights
Prerequisites:
- Basic understanding of machine learning concepts
- Familiarity with programming (Python preferred)
Recommended Resources:
- “Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps” by Valliappa Lakshmanan (Author), Sara Robinson (Author), Michael Munn (Author)
- Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps by Suhas Pote