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AWS for Machine Learning
AWS Machine Learning syllabus
AWS for Machine Learning course explores how to use the machine learning to solve a real business problem in a project-based learning environment.
AWS Syllabus
AWS Cloud Concepts
1
What is AWS?
2
Exploring AWS Accounts, Multi-Account Strategy, and AWS Organizations
3
AWS Identity and Access Management
4
Overview of AWS Services (Storage, Compute, Database)
5
Analytics on AWS
6
Automation and Deployment on AWS
7
Security in AWS
8
Billing and Pricing
9
AWS CLI
10
AWS Machine Learning Tech-Stack
11
⚙️ Project #1
12
⚙️ Project #2
13
AWS services for ingesting data (Amazon Database Migration Service (DMS), Amazon Kinesis)
14
Detailed overview of AWS S3
15
Use case: Migrating Oracle database to AWS using DMS
16
AWS services for transforming data (Lambda, Glue, Amazon EMR)
17
⚙️ Project #3
18
Use case: Data preprocessing with AWS Glue
19
AWS services for consuming data (Amazon Athena, Amazon QuickSight)
20
Use case: Uploading dataset to S3, querying it with Amazon Athena
21
Exploring the Amazon SageMaker, setting up Amazon SageMaker on
local machine
22
⚙️ Project #4
23
Amazon SageMaker Processing
24
Amazon SageMaker Autopilot
25
Built-in algorithms in Amazon SageMaker
26
⚙️ Project #5
27
Training models on AWS Sagemaker
28
Use case: Training Computer Vision Models on AWS Sagemaker
29
Use case: Training NLP Models on AWS Sagemaker
30
Training and deploying custom algorithms on AWS Sagemaker
31
⚙️ Project #6
32
Scaling training jobs with pipe mode and distributed training
33
Optimizing hyperparameters with Automatic Model Tuning
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🎉 Awesome! You already know the concepts, terminology, and the fundamentals of machine learning on AWS. You have successfully built, trained, evaluated, tuned, and deployed an ML model using AWS that solves their selected business problem.
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