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AWS Machine Learning Engineer Crash Course

Published by Pearson

Intermediate to advanced content levelIntermediate to advanced

Learn core Machine Learning skills and prepare for the exam

  • End-to-End ML Workflow Coverage: Learn the full ML lifecycle—from data prep to monitoring—so you're ready for both the exam and real-world projects.
  • Exam-Focused Learning: Aligned with the MLA-C01 exam guide to ensure you master exactly what’s needed to pass and succeed.
  • Hands-On AWS Labs: Gain practical experience by building, deploying, and managing real ML workflows using AWS services.

Gaining core Machine Learning skills has never been more important. This 5-hour / one-day course is designed to get you up and running with Machine Learning while also preparing you for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. The course covers the key content domains as outlined in the exam guide, including data preparation, ML model development, deployment and orchestration of ML workflows, and ML solution monitoring, maintenance, and security. It will provide hands-on experience through labs while also diving into theoretical knowledge so you can effectively build, deploy, and manage ML solutions using AWS services.

By the end of this training, you will be equipped with the knowledge and skills to design, deploy, and monitor machine learning solutions in AWS, making you an asset in any organization working with cloud-based ML systems.

What you’ll learn and how you can apply it

  • Ingest, Prepare, and Transform Data for ML Using SageMaker Tools
  • Build and Train Machine Learning Models with SageMaker
  • Deploy and Automate ML Pipelines
  • Monitor, Maintain, and Secure ML Solutions on AWS
  • Prepare for and Pass the AWS Certified Machine Learning Engineer - Associate Exam

This live event is for you because...

  • This training is designed for software developers, data scientists, ML engineers, and DevOps professionals who are looking to enhance their machine learning skills in the AWS ecosystem and earn the AWS Certified Machine Learning Engineer - Associate certification.

Prerequisites

  • Basic knowledge of Python programming
  • Familiarity with AWS core services (EC2, S3, IAM)
  • Understanding of basic statistical concepts

Course Set-up

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Data ingestion and storage (30 min)

  • Common Data Formats
  • Storing Data in S3
  • Storage Optimization and Transfer
  • Lab: Configuring AWS CLI and Storing Data in S3
  • Q&A

Segment 2: Data transformation and feature engineering (40 min)

  • Data Cleaning and Preprocessing with Data Wrangler
  • Feature Scaling and Encoding
  • Validating Data Quality
  • SageMaker Feature Store
  • Lab: Data Preparation Lab
  • Q&A

Break (10 min)

Segment 3: ML Model Development and Training (45 min)

  • Built-in Algorithms and JumpStart Models
  • Setting Up and Running SageMaker Training Jobs
  • Hyperparameter Tuning
  • A/B Testing and Model Evaluation
  • Model Versioning
  • Lab: SageMaker Model Preparation and Training
  • Q&A

Segment 4: Deployment and orchestration (40 min)

  • Real-time, Batch, and Asynchronous Inference
  • Integrating Data Processing and Training Steps
  • Using ML Pipelines
  • SageMaker Neo Edge Deployment
  • Lab: SageMaker Pipelines
  • Q&A

Break (10 min)

Segment 5: ML solution monitoring, maintenance and security (40 min)

  • CloudWatch Monitoring and Alerting
  • Cost Optimization with Auto-scaling
  • SageMaker Model Monitor
  • Lab: Cloudwatch Monitoring and Alerting
  • Q&A

Segment 6: IAM, VPC, EC2 and other core services (45 min)

  • IAM
  • VPC
  • EC2
  • SQS
  • SNS
  • Bedrock
  • Lab: Core Services Review in the Web Console
  • Q&A

Segment 7: Exam Overview and Preparation (30 min)

  • Test Specifics
  • Question Types
  • Time Management
  • Lab: Sample Questions Review

Final Q&A and Wrap Up (10 min)

Your Instructor

  • Nick Garner

    Nick Garner, CCIE #17871, is a Solutions Integration Architect with Cisco Systems supporting customers in large-scale network design. He has deployed and supported large-scale data center designs for prominent clients in the San Francisco Bay area and works with Amazon Web Services daily. Prior to Cisco he spent some time with a Cisco partner, Denali Advanced Integration and is also a founder of several IoT companies that operate exclusively in AWS. He is also a veteran of the United States Marine Corps.

Skill covered

Machine Learning