Here is the extensive and detailed course curriculum for Scaling AI-Powered Applications: Mastering MLOps and Model Serving for High-Performance Teams:
Course Overview This comprehensive course is designed to help high-performance teams master the art of scaling AI-powered applications using MLOps and model serving. Participants will gain hands-on experience with the latest tools and technologies, and receive a certificate upon completion issued by The Art of Service.
Scaling AI-Powered Applications: Mastering MLOps and Model Serving for High-Performance Teams
Course Overview This comprehensive course is designed to help high-performance teams master the art of scaling AI-powered applications using MLOps and model serving. Participants will gain hands-on experience with the latest tools and technologies, and receive a certificate upon completion issued by The Art of Service.
Course Curriculum Module 1: Introduction to MLOps and Model Serving
- Defining MLOps and its role in AI-powered applications
- Understanding the importance of model serving in MLOps
- Overview of key MLOps tools and technologies
- Introduction to model serving platforms and frameworks
Module 2: MLOps Fundamentals
- MLOps workflows and pipelines
- Data preparation and feature engineering
- Model training and hyperparameter tuning
- Model evaluation and selection
- MLOps best practices and design patterns
Module 3: Model Serving Fundamentals
- Model serving architectures and design patterns
- Model deployment and serving platforms
- Model monitoring and logging
- Model updating and maintenance
- Model serving best practices and challenges
Module 4: Scaling MLOps and Model Serving
- Scaling MLOps workflows and pipelines
- Distributed training and hyperparameter tuning
- Scaling model serving and deployment
- Load balancing and autoscaling
- High availability and disaster recovery
Module 5: Advanced MLOps and Model Serving Topics
- Explainability and interpretability in MLOps
- Fairness and bias in MLOps
- Security and compliance in MLOps
- Multi-cloud and hybrid MLOps
- Edge AI and MLOps
Module 6: Case Studies and Real-World Applications
- Real-world MLOps and model serving use cases
- Case studies of successful MLOps and model serving implementations
- Lessons learned and best practices from industry experts
- Group discussions and project work
Module 7: Hands-on Projects and Assignments
- Hands-on MLOps and model serving projects
- Assignments and quizzes to reinforce learning
- Personalized feedback and coaching
- Opportunity to work on real-world projects
Course Features - Interactive and Engaging: Interactive lessons, quizzes, and assignments to keep you engaged and motivated
- Comprehensive and Personalized: Comprehensive curriculum with personalized feedback and coaching
- Up-to-date and Practical: Up-to-date and practical knowledge and skills that can be applied immediately
- Real-world Applications: Real-world applications and case studies to reinforce learning
- High-quality Content: High-quality content created by expert instructors
- Certification: Certificate of Completion issued by The Art of Service
- Flexible Learning: Flexible learning schedule with lifetime access to course materials
- User-friendly and Mobile-accessible: User-friendly and mobile-accessible course platform
- Community-driven: Community-driven discussion forums and group work
- Actionable Insights: Actionable insights and hands-on projects to reinforce learning
- Lifetime Access: Lifetime access to course materials and updates
- Gamification and Progress Tracking: Gamification and progress tracking to keep you motivated
Certificate of Completion Upon completing the course, participants will receive a Certificate of Completion issued by The Art of Service. This certificate will demonstrate your expertise and knowledge in scaling AI-powered applications using MLOps and model serving.
Module 1: Introduction to MLOps and Model Serving
- Defining MLOps and its role in AI-powered applications
- Understanding the importance of model serving in MLOps
- Overview of key MLOps tools and technologies
- Introduction to model serving platforms and frameworks
Module 2: MLOps Fundamentals
- MLOps workflows and pipelines
- Data preparation and feature engineering
- Model training and hyperparameter tuning
- Model evaluation and selection
- MLOps best practices and design patterns
Module 3: Model Serving Fundamentals
- Model serving architectures and design patterns
- Model deployment and serving platforms
- Model monitoring and logging
- Model updating and maintenance
- Model serving best practices and challenges
Module 4: Scaling MLOps and Model Serving
- Scaling MLOps workflows and pipelines
- Distributed training and hyperparameter tuning
- Scaling model serving and deployment
- Load balancing and autoscaling
- High availability and disaster recovery
Module 5: Advanced MLOps and Model Serving Topics
- Explainability and interpretability in MLOps
- Fairness and bias in MLOps
- Security and compliance in MLOps
- Multi-cloud and hybrid MLOps
- Edge AI and MLOps
Module 6: Case Studies and Real-World Applications
- Real-world MLOps and model serving use cases
- Case studies of successful MLOps and model serving implementations
- Lessons learned and best practices from industry experts
- Group discussions and project work
Module 7: Hands-on Projects and Assignments
- Hands-on MLOps and model serving projects
- Assignments and quizzes to reinforce learning
- Personalized feedback and coaching
- Opportunity to work on real-world projects