AWS Machine Learning Specialty Course
A structured AWS Machine Learning Specialty course covering data engineering, model training, security, deployment, monitoring, MLOps and optimization, with linked practice questions.
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What you will learn
- Describe data engineering and preparation.
- Explain model training and algorithms.
- Describe security and compliance.
- Explain deployment and inference.
- Describe monitoring, MLOps and optimization.
Before you start
- Basic AWS knowledge is helpful.
- Some Python or ML fundamentals are recommended.
- No previous AWS certification is required.
Lesson 1 Data Engineering and Preparation
The AWS Machine Learning Specialty exam validates designing, building, and operating ML solutions on AWS. Data engineering is the first step: S3 stores raw datasets, Athena queries data directly in S3, and AWS Glue prepares and catalogs data. A feature store centralizes reusable features, and Data Wrangler provides visual preparation. Teams split data into training, validation, and test sets, normalize numeric features, handle categorical variables, and avoid data leakage. Quality data preparation directly affects model accuracy and reliability.
Example
Example: A team stores raw transactions in S3, uses Glue to clean them, creates a feature store for reusable features, and splits the final dataset into training, validation, and test sets.Lesson 2 Model Training and Algorithms
Model training uses SageMaker built-in algorithms and custom containers. Linear Learner supports regression, XGBoost handles tabular classification and regression, K-Means clusters, and PCA reduces dimensions. Hyperparameters are set before training, epochs are passes through data, and loss functions measure error. Overfitting occurs when the model memorizes training data. Early stopping, validation sets, transfer learning, and confusion matrices help build reliable models.
Example
Example: A regression team uses Linear Learner, monitors validation loss, applies early stopping, and evaluates the final model with a confusion matrix for a classification pilot.Lesson 3 Security and Compliance
Security and compliance protect data and models. IAM controls users and roles, KMS manages encryption keys, VPC provides network isolation, and VPC endpoints provide private access. TLS protects data in transit, CloudTrail audits API calls, and least privilege grants only required permissions. Notebook instances use IAM roles, and private endpoints keep inference inside the VPC. Encrypting data at rest and in transit is a core requirement for ML workloads.
Example
Example: A healthcare ML workload uses KMS encryption, runs inside a VPC, uses a private endpoint, and allows only the minimum IAM permissions required.Lesson 4 Deployment and Inference
Deployment and inference put models into production. Real-time endpoints serve live predictions, batch transform processes large datasets, and inference pipelines chain preprocessing and prediction. Endpoint configurations define model, instance, and capacity; autoscaling adjusts capacity; production variants route traffic for A/B testing. Serverless and batch options can reduce cost and match workload patterns.
Example
Example: A retailer uses a real-time endpoint for product recommendations, batch transform for nightly scoring, and autoscaling to handle traffic peaks.Lesson 5 Monitoring, MLOps, and Optimization
Monitoring and MLOps keep models reliable. CloudWatch monitors endpoint metrics and alarms; Model Monitor detects data drift; Experiments tracks runs; Pipelines automates workflows; lineage records artifact relationships; and Clarify detects bias. Optimization uses Spot training, distributed training, Autopilot, automatic tuning, right-sized instances, model compression, and cost review. Teams retrain when drift appears and use MLOps to make updates repeatable.