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

Subjects

Data Science