ML Exam Prep - Sagemaker AI

 

ML Associate Exam Prep 

Sagemaker AI

• SageMaker AI 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 and are well supported in AWS
• 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.

Intro to SageMaker AI

• AWS service that can handle the whole E2E process in ML
  • E2E ML process = Data processing, model training, model deployment, and model hosting
  • Tons of features and sub-products (will go into depth in these notes)
• SageMaker Training and Deployment Architecture
• Input/output data usually in S3, but could be in other data stores
• Training and inference code must be inside container images registered in ECR
• Not all ML models will be deployed to endpoints.

E2E ML Process in SageMaker

1. Data Preparation (data prep)
   • Data usually comes from S3
     • Data can also come from Athena, EMR, Redshift, Amazon Keyspaces DB…
   • Integration with Apache Spark
2. Data Processing
   • Processing job: copy raw data from S3 → Spin up processing container → Output processed data to S3
   • Container can be SageMaker built-in or user provided (code)
3. Training
   • Training job requires
     • URL of S3 bucket with training data
     • ML compute resources
     • URL of S3 bucket for output → Model outputted to S3
     • Container (ECR) path to training code
   • Many training options available
     • Built-in algorithms, Spark MLLib, Tensorflow, PyTorch, Scikit-learn, XGBoost, Hugging Face, your own Docker image, AWS marketplace-purchased algorithms…
4. Deployment
• 2 ways:
  • Persistent endpoint for individual predictions/inference on demand
  • SageMaker Batch Transform for predictions of an entire dataset
• Many cool options: inference pipelines, SageMaker Neo (edge devices), Elastic Inference, automatic scaling, shadow testing…

SageMaker Domain

• 🔧 Organizational unit within SageMaker → organize users, apps, and resources
  • A domain must be configured before you can do anything in SageMaker!
    • Think of it as an isolation of an ML project
• Each domain has one EFS volume
  • Each user has their private EFS directory within that volume
  • There's a shared EFS directory available to all users
• User profile: represents an individual user/person in a domain
  • Can create own personal apps
  • Can spin private SageMaker Studio instances
  • Has access to a private EFS directory to store personal files
• Shared resources across all users:
  • Shared spaces
  • Shared EFS directory
  • Communal IDE app (SageMaker Studio public to all users)

Network Configuration in SageMaker Domain

• By default, a domain has two VPCs
1. One with public internet access → can expose public endpoints for your domain
   • Managed by SageMaker
   • Optional → can select “VPC Only” when creating the domain, which means this managed VPC is NOT created
2. One for private traffic
• Encrypted traffic to domain's EFS volume
• YOU manage it: must specify the VPC, its subnets, and security groups (SGs)

Interfaces for Using SageMaker

SageMaker Notebooks

• 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 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

SageMaker SDKs

• Training and deployment of ML models via Python scripts
• Python API libraries → import inside your code
  1. Boto3 (low-level API)
  2. SageMaker Python SDK (high-level API)
• Can automate ML workflows, manage training jobs, deployments, and pipelines

SageMaker Studio

• Web-based IDE for E2E ML development

• Features

  • Team collaboration
  • Tune and debug ML models
  • Deploy ML models
  • Automated workflows

• SageMaker Studio Screenshot

SageMaker Console UI

• AWS Management Console interface for SageMaker

  • GUI for managing SageMaker resources

• Mostly for administrative tasks

• Can access all other interfaces from the console UI

• SageMaker Console UI Screenshot 

SageMaker Jumpstart 

• ML Hub with many pre-trained ML models and pre-built ML solutions

  • Offers one-click deployment of models for inference
  • End-to-end solutions for common business problems

• Computer Vision (CV) models, Natural Language Processing (NLP) models, GenAI Foundation Models (FMs)…

  • Amazon-owned models or 3rd-party provider models
  • Provider examples: HuggingFace, Databricks, Meta…

• SageMaker Jumpstart Screenshot

  
  
  

  
  

SageMaker Canvas

• No-code ML for business analysts

• Features

  • Build custom ML model (leverages AutoML powered by SageMaker Autopilot)
    • e.g. Upload CSV data (CSV-only for now), select column to predict & build model
  • Automatic data cleaning (leverages Data Wrangler)
  • Access ready-to-use models from AWS AI services (Rekognition, Comprehend…)
  • GenAI support via Bedrock or JumpStart FMs

• SageMaker Canvas Screenshot

  
  
  

  
  

Summary Table of Interfaces for SageMaker