Machine Learning Engineering on AWS

Price
$2,025.00 USD

Duration
3 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course 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.

Course Objectives

  • 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.
  • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
  • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
  • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
  • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
  • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
  • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

If you have questions about Learning Credits, please contact your Account Manager.

Corporate Tech Pass: Our Corporate Tech Pass includes unlimited attendance for a single person, in the following Virtual Instructor Led course types: Microsoft Office, Microsoft Technical, CompTIA, Project Management, SharePoint, ITIL, Certified Ethical Hacker, Certified Hacking Forensics Investigator, Java, Professional Development Courses and more. The full list of eligible course titles can be found at https://www.newhorizons.com/eligible.

If you have questions about our Corporate Tech Pass, please contact your Account Manager.

Course Prerequisites

  • 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)

Agenda

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

Module 5: Choosing a Modeling Approach

  • Topic A: Amazon SageMaker AI built-in algorithms
  • Topic B: Selecting built-in training algorithms
  • Topic C: Amazon SageMaker Autopilot
  • 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 A/B

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

 

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