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Overview

Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.

Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.
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What You'll Learn

In this course, you will learn to do the following:

  • Explain ML fundamentals and its applications in the AWS Cloud.
  • Process, transform, and engineer data for ML tasks by using AWS services.
  • Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.
  • Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.
  • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
  • Discuss appropriate security measures for ML resources on AWS.
  • Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.

Who Should Attend

This course is designed for professionals who are interested in building, deploying, and operationalizing machine learning models on AWS. This could include current and in-training machine learning engineers who might have little prior experience with AWS. Other roles that can benefit from this training are DevOps engineer, developer, and SysOps engineer.
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Prerequisites

We recommend that attendees of this course have the following:

  • Familiarity with basic machine learning concepts
  • Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn
  • Basic understanding of cloud computing concepts and familiarity with AWS
  • Experience with version control systems such as Git (beneficial but not required)

Learning Journey

Coming Soon...

Day 1

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS

  • Topic A: Introduction to ML
  • Topic B: Amazon SageMaker AI
  • Topic C: Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges

  • Topic A: Evaluating ML business challenges
  • Topic B: ML training approaches
  • Topic C: ML training algorithms

Module 3: Data Processing for Machine Learning (ML)

  • Topic A: Data preparation and types
  • Topic B: Exploratory data analysis
  • Topic C: AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering

  • Topic A: Handling incorrect, duplicated, and missing data
  • Topic B: Feature engineering concepts
  • Topic C: Feature selection techniques
  • Topic D: AWS data transformation services
  • Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
  • Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Day 2

Module 5: Choosing a Modeling Approach

  • Topic A: Amazon SageMaker AI built-in algorithms
  • Topic B: Amazon SageMaker Autopilot
  • Topic C: Selecting built-in training algorithms
  • Topic D: Model selection considerations
  • Topic E: ML cost considerations

Module 6: Training Machine Learning (ML) Models

  • Topic A: Model training concepts
  • Topic B: Training models in Amazon SageMaker AI
  • Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models

  • Topic A: Evaluating model performance
  • Topic B: Techniques to reduce training time
  • Topic C: Hyperparameter tuning techniques
  • Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies

  • Topic A: Deployment considerations and target options
  • Topic B: Deployment strategies
  • Topic C: Choosing a model inference strategy
  • Topic D: Container and instance types for inference
  • Lab 5: Shifting Traffic

Day 3

Module 9: Securing AWS Machine Learning (ML) Resources

  • Topic A: Access control
  • Topic B: Network access controls for ML resources
  • Topic C: Security considerations for CI/CD pipelines

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

  • Topic A: Introduction to MLOps
  • Topic B: Automating testing in CI/CD pipelines
  • Topic C: Continuous delivery services
  • Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

Module 11: Monitoring Model Performance and Data Quality

  • Topic A: Detecting drift in ML models
  • Topic B: SageMaker Model Monitor
  • Topic C: Monitoring for data quality and model quality
  • Topic D: Automated remediation and troubleshooting
  • Lab 7: Monitoring a Model for Data Drift

Module 12: Course Wrap-up

Frequently Asked Questions (FAQs)

  • Why get AWS certified?

    AWS certifications validate your expertise in cloud computing and your proficiency in using AWS services.

    These certifications are globally recognized and highly sought after by employers, as they demonstrate your ability to design, deploy, and manage scalable and secure cloud solutions on the AWS platform.

    AWS-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    AWS offers a variety of certification exams at different levels (Foundational, Associate, Professional, and Specialty) covering various domains and services.

    The exams typically consist of multiple-choice and multiple-response questions, and some may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is AWS certification valid for?

    AWS certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the exam or completing the AWS Cloud Quest game-based training (if option is applicable).

    Note: Certification requirements and policies may be updated by AWS from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 24 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Course Curriculum

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Frequently Asked Questions

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