Machine Learning on AWS

Overview

Gain hands-on experience with AWS machine learning services through gamified, real-world scenarios and interactive challenges designed for ML engineers and data scientists.

Syllabus

- Introduction to AWS Machine Learning -- Overview of AWS Machine Learning services -- Setting up your AWS account and environment -- Introduction to Amazon SageMaker - Data Preprocessing and Management -- Storing and retrieving data with Amazon S3 -- Using AWS Glue for ETL processes -- Data preparation and feature engineering in SageMaker - Building Machine Learning Models -- Introduction to built-in algorithms in Amazon SageMaker -- Custom model development with Jupyter notebooks -- Utilizing SageMaker Studio for model development - Training Machine Learning Models -- Understanding managed training services -- Optimizing training jobs with hyperparameter tuning -- Distributed training and using spot instances - Model Evaluation and Validation -- Techniques for validating model performance -- Monitoring training jobs -- Practical tips for model evaluation on AWS - Deploying Machine Learning Models -- Model deployment options with SageMaker -- Real-time and batch inference -- A/B testing and endpoint scaling - Automation and CI/CD in AWS -- Implementing MLOps with SageMaker Pipelines -- Automating end-to-end workflows with AWS Step Functions -- Version control and continuous deployment with AWS CodePipeline - Security and Compliance in AWS ML -- Security best practices for AWS ML services -- Managing IAM roles and policies -- Data encryption and compliance considerations - Real-World Scenarios and Challenges -- Gamified exercises using real-world industry data sets -- Interactive ML challenges and competitions -- Case studies on successful ML implementations on AWS - Capstone Project -- Design and implement a complete ML solution on AWS -- Presentation and demonstration of the project -- Peer review and feedback - Conclusion and Next Steps -- Resources for further learning in machine learning on AWS -- Career paths and certification opportunities -- Q&A and course wrap-up

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