Amazon SageMaker: Introduction

💡 SageMaker is the “heart” of the MLA-C01 certification

The majority of exam questions will have to do with SageMaker, and knowing it inside and out will be essential to do well in the exam.
It is important to understand and discern between SageMaker Processing, SageMaker Training and SageMaker Hosting, which all cover different aspects of the end-to-end ML process.
These notes first cover generic ML knowledge and concepts, and then their implementation in AWS (usually involving SageMaker and other AWS services).
Some open-source Apache services like Hadoop or Spark are also covered, since they are also popular in ML environments.
It is a good idea to review the high-level overview of SageMaker that was done in the foundational AIF-C01 certification. MLA-C01 builds on top of that knowledge.

SageMaker Generic Overview

💡 Will only include notes on new details and insights!
‼️ NOTE: in 2025, AWS rebranded SageMaker as "SageMaker AI”
- If you're looking for it in the AWS Console, look for it under that new name
- Just plain "SageMaker" takes you instead to "SageMaker Unified Studio" or the "SageMaker Platform," which is a wrapper around SageMaker and Amazon DataZone
- The exam only covers SageMaker AI at this time

🔧 Old/classic method for ML in SageMaker → ML code
Spin up EC2 instances to host ML Notebooks, which direct ML E2E process:
- S3 data access
- ML code in notebook (akin to Jupyter notebook)
  - Libraries like Scikit_learn, numpy, pandas, Apache Spark, Tensorflow, etc at your disposal
  - Wide variety of built-in models
- Can spin up training instances
- Can deploy trained models for making predictions (inferring) at scale
Alternative to Notebook: spin up ML models without code by using SageMaker console
SageMaker Notebook Diagram

Data prep
- Data usually comes from S3
  - Data can also come from Athena, EMR, Redshift, Amazon Keyspaces DB…
- Integration with Apache Spark