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AWS Machine Learning Engineer Associate (MLA-C01) Bootcamp

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Get ready for the exam

Course outcomes

  • Gain experience in AWS machine learning services like SageMaker, Bedrock, and other critical tools required for the AWS Certified Machine Learning Engineer Associate Exam
  • Learn practical skills in data ingestion, transformation, feature engineering, and model deployment through hands-on exercises and real-world scenarios
  • Develop an understanding of model tuning, evaluation, monitoring, and security practices to ensure optimal performance and compliance in ML solutions on AWS
  • Increase your confidence in earning certification with comprehensive exam preparation and practice questions

Course description

Join industry expert Dr. Yasir Khan to prepare for the AWS Certified Machine Learning Engineer Associate Exam. You’ll dive deep into Amazon's ML services, including SageMaker and Bedrock, and gain hands-on experience through practical exercises and demos. You’ll explore the entire ML pipeline on AWS, from data ingestion and transformation to model training, tuning, and deployment. You’ll learn to secure your AWS environment, monitor ML models, and optimize costs, ensuring you’re fully equipped for the exam. Whether you’re a data engineer, data scientist, or DevOps professional, you’ll elevate your AWS ML expertise.

NOTE: With today’s registration, you’ll be signed up for both sessions. Although you can attend any of the sessions individually, we recommend participating in both.

What you’ll learn and how you can apply it

  • Learn to leverage SageMaker, Bedrock, and other AWS services to build and deploy scalable ML models
  • Develop skills in ingesting, transforming, and validating data for ML, ensuring high-quality, ready-to-use datasets
  • Explore advanced techniques in model tuning, performance analysis, and deployment, applicable to real-world AWS ML projects
  • Gain expertise in monitoring ML models, securing AWS environments, and optimizing operational costs, crucial for maintaining robust ML systems
  • Apply your knowledge through quizzes and hands-on exercises that mirror the AWS certification exam format, ensuring you’re ready for the test

This live event is for you because...

  • You’re preparing for the AWS Certified Machine Learning Engineer Associate Exam.
  • You value practical, hands-on experience and want to apply your learning directly in real-world scenarios.
  • You’re an IT professional looking to deepen your AWS ML expertise and advance your career with a recognized certification.

Prerequisites

  • Prior experience with AWS services, especially in compute, storage, and security
  • A foundational understanding of machine learning concepts, algorithms, and best practices
  • Proficiency in programming languages like Python
  • Knowledge of data processing techniques and tools to understand data ingestion and transformation processes

Recommended preparation:

Recommended follow-up:

  • Read and follow AWS Certified Machine Learning Engineer - Associate Exam Guide

Schedule

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

Day 1: AWS ML Foundations, Data Preparation, and Model Development

Introduction to AWS ML engineering (10 minutes)

  • Presentation: Setting up an AWS account
  • Q&A

Introduction to data and storage solutions (45 minutes)

  • Presentation: Data warehouses, lakes and lakehouses; data mesh, ETL/ELT pipelines; Amazon S3; encryption; access points; EBS, EFS, and FSx
  • Q&A
  • Break

Data streaming and data integrity (40 minutes)

  • Presentation: Amazon Kinesis, MSK, EMR, Spark, and AWS Glue
  • Q&A

Exploring SageMaker and built-in algorithms (45 minutes)

  • Presentation: Introduction to SageMaker and built-in algorithms
  • Q&A
  • Break

Feature engineering and machine learning (50 minutes)

  • Presentation: Feature engineering; deep learning; model training; model tuning and evaluation techniques
  • Q&A

Foundations of GenAI models and Amazon Bedrock (50 minutes)

  • Presentation: Fundamentals; transformer architecture; GPT and fine-tuning techniques; Amazon Bedrock and foundation models
  • Q&A

Day 2: Deployment, Orchestration, Monitoring, Maintenance, and Security

AWS managed AI services (40 minutes)

  • Presentation: Amazon Comprehend, Translate, Transcribe, Polly, and Rekognition
  • Q&A

Operationalizing machine learning with AWS MLOps (40 minutes)

  • Presentation: SageMaker deployment techniques; Amazon ECS, EKS, CloudFormation, and CDK
  • Q&A
  • Break

Ensuring security, identity, and compliance in AWS ML (40 minutes)

  • Presentation: Encryption VPCs; IAM; logging; monitoring
  • Q&A

Effective monitoring, governance and best practices for ML projects (40 minutes)

  • Presentation: CloudWatch; CloudTrail; Amazon QuickSight
  • Q&A
  • Break

Best practices for machine learning on AWS (40 minutes)

  • Presentation: Responsible AI; ML design principles and lifecycle
  • Q&A

Sample certification exam questions (20 minutes)

  • Group discussion: Sample exam questions
  • Q&A

Wrap-up and certification tips (20 minutes)

  • Presentation: Key takeaways; exam strategies
  • Q&A

Your Instructor

  • Yasir Khan

    Dr. Yasir Khan is the founder of 38 Labs, an Enterprise Data & AI consulting group with offices based out of Paris, New York and Bangalore. He holds a PhD in AI and is an instructor at O’Reilly Media mentoring future experts on AI transformation, machine learning, enterprise solutions and digital transformation. Over his career he has published several articles for leading publishing houses in the field of AI. He speaks at several international conferences such as PyCon, PyData, IEEE. In his spare time he likes flying aircrafts, climbing mountains and traveling.

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Skills covered

  • Machine Learning
  • AWS AI & Machine Learning