AWS Model Training, Optimization & Deployment

AWS: Model Training, Optimization & Deployment is the third course in the Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course is designed to equip learners with the skills to train, optimize, and deploy machine learning models efficiently using AWS services. Learners begin by exploring popular algorithms such as Linear Learner, XGBoost, LightGBM, and k-Nearest Neighbors (k-NN), and understand their use cases in classification and regression tasks.You’ll then dive into the model training process, learning how to configure key parameters like epochs, batch size, and steps for optimized performance. Then the learners will begin by exploring SageMaker Model Debugger and SageMaker Experiments, which help monitor training jobs and compare experiment results efficiently.You’ll then dive into cross-validation techniques and learn how to apply hyperparameter tuning using both random search and Bayesian optimization methods to improve model accuracy. Finally by exploring compute options such as Amazon ECS, Amazon EKS, and AWS Lambda, followed by infrastructure management with AWS CloudFormation.You’ll learn how to implement auto scaling policies for ML workloads and choose the right SageMaker compute instance types (CPU vs. GPU) for different deployment scenarios. This course is divided into three comprehensive modules, each containing targeted lessons and practical demonstrations. Learners will benefit from approximately 3.5 to 4 hours of expert-led video content, featuring real-world use cases and hands-on walkthroughs using AWS tools. Every module includes Graded and Ungraded Quizzes to assess conceptual understanding and application. Module 1: Model Training, Algorithms & Inference Techniques Module 2: Model Optimization, Evaluation & Tuning with SageMaker Module 3: Scalable Infrastructure & Automated ML Deployment on AWS By the end of this course, learners will be able to: Compare real-time and batch inference approaches to determine the best strategy for model deployment. Apply model optimization techniques such as hyperparameter tuning Understand and select appropriate inference strategies for deployment Explore AWS compute and orchestration services like ECS, EKS, Lambda, and CloudFormation for ML deployment. This course is ideal for ML practitioners, data scientists, and cloud developers who are looking to scale their ML workflows and gain hands-on experience with advanced features of Amazon SageMaker. It is also designed for learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam, focusing on the model training and deployment aspects of the certification

• Explore built-in algorithms in Amazon SageMaker such as Linear Learner, XGBoost, LightGBM, and k-NN for ML model development
• Configure key training parameters like epochs, batch size, and steps to train and evaluate ML models effectively
• Compare real-time and batch inference approaches to determine the best strategy for model deployment

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work