About
This comprehensive program, "Machine Learning Operations (MLOps) for Generative AI," is designed to equip learners with the essential skills and knowledge to effectively manage and deploy generative AI models using MLOps practices. The course covers a wide range of topics, from the fundamentals of machine learning and generative AI to advanced techniques and real-world applications. This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to deepen their understanding of MLOps and generative AI. By the end of the course, participants will be well-equipped to design, deploy, and maintain robust generative AI models in a production environment. Table of Contents Chapter 1: Introduction to MLOps Chapter 2: Fundamentals of Machine Learning Chapter 3: Generative AI Basics Chapter 4: Data Management and Preprocessing Chapter 5: Model Development and Training Chapter 6: Version Control and Experiment Tracking Chapter 7: Continuous Integration and Continuous Deployment (CI/CD) Chapter 8: Model Serving and Deployment Chapter 9: Monitoring and Logging Chapter 10: Model Maintenance and Retraining Chapter 11: Security and Compliance Chapter 12: Scalability and Performance Optimization Chapter 13: Cost Management and Optimization Chapter 14: Case Studies and Real-World Applications Chapter 15: Advanced Generative AI Techniques Chapter 16: Ethical Considerations in Generative AI Chapter 17: Collaboration and Team Dynamics Chapter 18: Industry Tools and Platforms Chapter 19: Future Trends in MLOps and Generative AI Chapter 20: Capstone Project