Executive Development Programme in Scaling Machine Learning Models on AWS SageMaker

This course is designed for data scientists, engineers, and business leaders aiming to enhance their skills in scaling machine learning models using AWS SageMaker. Participants will learn to optimize model performance, manage large datasets, and deploy models at scale efficiently.

They will gain practical skills in using AWS SageMaker’s advanced features, including model tuning, endpoint management, and cost optimization techniques, ensuring they can leverage AWS's capabilities to drive business growth through scalable machine learning solutions.

1. 1. Introduction to Machine Learning and AWS SageMaker: Learners will study the basics of machine learning and understand how to use AWS SageMaker for building and deploying models. They will gain foundational knowledge of ML and hands-on experience setting up SageMaker instances.
2. 2. Data Preparation and Management for ML: This module covers data cleaning, transformation, and management techniques essential for training ML models. Learners will learn to preprocess data using SageMaker and other AWS services, gaining practical skills in data preparation.
3. 3. Building and Training Machine Learning Models: Learners will explore various ML algorithms and techniques for building and training models on AWS SageMaker. They will gain skills in selecting appropriate algorithms, tuning hyperparameters, and understanding model performance metrics.
4. 4. Model Evaluation and Validation: This module focuses on evaluating and validating ML models to ensure they meet business requirements. Learners will learn to use various evaluation techniques and gain skills in interpreting model performance using SageMaker.
5. 5. Deploying ML Models on AWS SageMaker: Learners will learn how to deploy trained ML models as real-time endpoints using AWS SageMaker. They will gain hands-on experience in deploying models and setting up scalable inference pipelines.
6. 6. Scaling ML Models with SageMaker: This module covers strategies and techniques for scaling ML models to handle large volumes of data and traffic. Learners will learn to optimize model performance and scalability using SageMaker and related AWS services.
7. 7. Advanced SageMaker Features and Custom Models: Learners will explore advanced features of AWS SageMaker, including managed Jupyter notebooks, model tuning, and custom training algorithms. They will gain skills in leveraging these features to build and deploy more complex ML models.
8. 8. Monitoring and Maintenance of ML Models: This module covers best practices for monitoring and maintaining deployed ML models to ensure they remain accurate and reliable. Learners will learn to set up monitoring and logging using SageMaker and other AWS services.
9. 9. Cost Optimization for ML Workloads: Learners will learn how to optimize costs for ML workloads on AWS, including efficient use of SageMaker resources and cost-effective model deployment strategies. They will gain skills in cost management and resource optimization.
10. 10. Case Studies and Practical Applications: In this final module, learners will work on real-world case studies and practical applications of ML on AWS SageMaker. They will apply the skills and knowledge gained throughout the programme to solve complex business problems and gain experience in end-to-end ML project management.