Curriculum Overview: AWS Services for the ML Pipeline
Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker AI, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Model Monitor)
Prerequisites
Before embarking on this curriculum to identify and utilize AWS services across the Machine Learning (ML) pipeline, learners should possess the following foundational knowledge:
- Basic AI/ML Concepts: Understanding of the general ML lifecycle (data collection, training, evaluation, deployment).
- Cloud Computing Fundamentals: Familiarity with basic AWS infrastructure concepts, such as IAM roles, S3 storage, and compute instances.
- Data Processing Basics: High-level understanding of what feature engineering, datasets, and data transformations entail.
- Python/Jupyter Notebooks: Basic ability to read Python code and navigate Jupyter Notebook environments, which are heavily used in Amazon SageMaker.
[!NOTE] This curriculum aligns directly with the AWS Certified AI Practitioner (AIF-C01) exam objectives, specifically Task Statement 1.3: Identify relevant AWS services and features for each stage of an ML pipeline.
Module Breakdown
This curriculum is divided into sequential modules that mirror the real-world machine learning lifecycle, progressively increasing in technical depth.
| Module | Topic | Difficulty | Key AWS Services Covered |
|---|---|---|---|
| Module 1 | Data Preparation & Feature Engineering | ⭐ | SageMaker Data Wrangler, SageMaker Processing, SageMaker Feature Store |
| Module 2 | Model Building & Experimentation | ⭐⭐ | SageMaker Studio Classic, SageMaker Notebooks, SageMaker JumpStart |
| Module 3 | Model Training & Evaluation | ⭐⭐⭐ | SageMaker AI, SageMaker Experiments, SageMaker Clarify |
| Module 4 | Deployment & Registry | ⭐⭐⭐ | SageMaker Model Registry, MLFlow on SageMaker |
| Module 5 | Monitoring & MLOps Orchestration | ⭐⭐⭐⭐ | SageMaker Model Monitor, SageMaker Pipelines, AWS Step Functions |
Learning Objectives per Module
Module 1: Data Preparation & Feature Engineering
- Use SageMaker Data Wrangler to clean, transform, and explore data from over 50 sources.
- Understand how to store, retrieve, and share ML features securely using SageMaker Feature Store.
- Identify when to automate data preprocessing tasks using SageMaker Processing.
Module 2: Model Building & Experimentation
- Navigate SageMaker Studio Classic to manage ML workflows in a unified web-based IDE.
- Deploy pre-trained models and built-in algorithms quickly using SageMaker JumpStart.
- Organize and track iterative model runs utilizing SageMaker Experiments.
Module 3: Model Training & Evaluation
- Configure training jobs using built-in algorithms or custom containers in SageMaker AI.
- Identify bias in datasets and explain model predictions leveraging SageMaker Clarify.
- Evaluate different model runs to identify the best performing models using MLFlow integration.
Module 4: Deployment & Registry
- Catalog models, manage versions, and handle production deployment approvals via SageMaker Model Registry.
- Describe methods to use a model in production, such as managed API endpoints.
Module 5: Monitoring & MLOps Orchestration
- Automatically detect data drift, model quality decline, and bias using SageMaker Model Monitor.
- Build, automate, and manage end-to-end ML workflows using SageMaker Pipelines.
- Trigger pipeline executions using AWS Step Functions or Lambda functions.
Success Metrics
To ensure you have mastered this curriculum, track your progress against these core success metrics:
- Service Mapping Accuracy: You can correctly identify the appropriate AWS service for any given scenario in the ML lifecycle with 90%+ accuracy.
- Pipeline Architecture Comprehension: You can draw and explain an end-to-end MLOps architecture using SageMaker components.
- Vocabulary Mastery: You can define and differentiate between similar services (e.g., Data Wrangler vs. Feature Store vs. Model Registry).
- Exam Readiness: You can consistently score 85% or higher on practice questions related to Task Statement 1.3 of the AIF-C01 exam.
Visualizing the Pipeline
Real-World Application
Understanding the AWS ML pipeline is not just about passing a certification; it solves critical business challenges in modern software development.
Why This Matters in Your Career
- Accelerated Time to Market: By leveraging MLOps tools like SageMaker Pipelines, organizations move models from experimental notebooks to scalable production environments much faster.
- Reduced Technical Debt: Standardizing the lifecycle using SageMaker Model Registry and Feature Store ensures repeatable processes, avoiding the "it works on my machine" problem.
- Automated Quality Assurance: Deployed models degrade over time as real-world data changes. Knowing how to implement SageMaker Model Monitor ensures businesses automatically catch data drift and declining performance before it impacts customers.
- Cross-Functional Collaboration: MLOps bridges the gap between Data Scientists (experimenting in Studio Classic), Data Engineers (building in Data Wrangler), and IT Operations (monitoring endpoints).
MLOps Feedback Loop
[!IMPORTANT] The ultimate goal of learning these specific AWS services is to build scalable, automated, and governed AI solutions. A strong grasp of SageMaker's modular capabilities makes you an invaluable asset to any cloud-native data team.