Executive Development Programme in AWS CI/CD for Machine Learning: Deploying Models at Scale

This course is tailored for IT leaders, data scientists, and engineering managers looking to enhance their organization’s machine learning (ML) model deployment capabilities using Amazon Web Services (AWS). Participants will gain hands-on experience in designing, implementing, and optimizing CI/CD pipelines for ML models, ensuring efficient and scalable deployment across various environments.

Upon completion, learners will be proficient in using AWS services such as CodeCommit, CodeBuild, CodePipeline, and SageMaker to automate model training, validation, and deployment. They will also understand best practices for monitoring, scaling, and maintaining ML models in production, equipping them to lead their teams towards more robust and scalable ML solutions.

1. 1. Introduction to AWS CI/CD and Machine Learning: Learners will understand the basics of AWS CI/CD pipelines and machine learning, including core ML concepts and AWS services relevant to ML development. They will gain foundational knowledge to set up and manage ML projects on AWS.
2. 2. AWS Fundamentals for Machine Learning: This module covers essential AWS services and concepts needed for machine learning, such as S3 for storage, RDS for databases, and EC2 for compute. Learners will learn how to use these services effectively for ML workflows.
3. 3. Building CI/CD Pipelines for ML Models: Learners will design and implement CI/CD pipelines using AWS CodePipeline, CodeBuild, and CodeDeploy. They will understand the importance of version control, automated testing, and deployment strategies for ML models.
4. 4. Model Training and Evaluation: This module focuses on training ML models using AWS SageMaker and evaluating their performance. Learners will learn to preprocess data, select appropriate algorithms, and fine-tune models for optimal performance.
5. 5. Model Deployment Strategies: Learners will explore various deployment strategies for ML models, including batch transformations, real-time endpoints, and model hosting. They will understand how to choose the right strategy based on the use case and performance requirements.
6. 6. Scaling ML Deployments: This module covers techniques for scaling ML deployments on AWS, including auto-scaling, load balancing, and leveraging AWS Lambda for serverless computing. Learners will learn to optimize ML model performance and handle high traffic scenarios.
7. 7. Monitoring and Logging ML Models: Learners will learn how to monitor ML models using CloudWatch and set up logging and alerting mechanisms. They will understand the importance of monitoring for maintaining model performance and detecting anomalies.
8. 8. Cost Optimization in ML Deployments: This module focuses on cost management and optimization techniques for ML deployments on AWS. Learners will learn to monitor and reduce costs through efficient resource utilization and cost-effective pricing models.
9. 9. Secure ML Deployments: Learners will cover security best practices for ML applications, including data encryption, access control, and secure communication channels. They will understand how to protect ML models and data from potential threats.
10. 10. Advanced Topics in ML Deployment: This module delves into advanced topics such as model versioning, A/B testing, and model drift detection. Learners will gain expertise in managing and maintaining ML models over time to ensure they remain effective and relevant.