AWS Machine Learning Engineer Associate (MLA-C01)

Name: AWS Machine Learning Engineer Associate (MLA-C01)
Availability: InStock

Master the full ML lifecycle on AWS to earn the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification. This course covers data preparation with SageMaker Data Wrangler and Feature Store, model training and tuning with SageMaker built-in algorithms and custom scripts, deployment strategies from real-time endpoints to batch transform, and end-to-end MLOps with SageMaker Pipelines, Model Monitor, and Model Registry.

4.80/5.0

16 hours

0 enrolled

Updated May 2026

Course Content ↓

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By Sara Okonkwo

What You'll Learn

Prepare and transform datasets for ML using AWS Glue, Athena, SageMaker Data Wrangler, and Feature Store

Train, evaluate, and tune ML models using SageMaker built-in algorithms, custom containers, and Automatic Model Tuning

Deploy ML models using real-time endpoints, serverless inference, batch transform, and multi-model endpoints

Build and automate ML pipelines using SageMaker Pipelines, Model Registry, and CI/CD integration

Monitor deployed models for data drift, model quality, and bias using SageMaker Model Monitor and Clarify

Apply security best practices including IAM roles, VPC isolation, encryption, and SageMaker governance

Prerequisites

Hands-on experience with AWS core services (S3, IAM, EC2, Lambda)
Python programming and familiarity with machine learning fundamentals (supervised/unsupervised learning, evaluation metrics)
Basic understanding of SQL and data processing concepts
AWS Cloud Practitioner or equivalent AWS experience recommended

About the Instructor

Sara Okonkwo

Expert instructor with hands-on industry experience in Ai Ml.

Included in paid plans

LevelIntermediate

Duration16 hours

Lessons—

Students0

Rating4.80 / 5.0

Cert targetMLA-C01

This course includes

Hands-on practice labs
AI-powered explanations
Progress tracking
Certificate of completion
Lifetime access

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