Machine Learning Engineering on AWS Training

Live Online & Classroom Enterprise Certification Training

Learn to build, train, and deploy scalable ML solutions using AWS tools like SageMaker, covering the full machine learning lifecycle.

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What is Machine Learning Engineering on AWS Certification Training about?

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

What are the objectives of Machine Learning Engineering on AWS Certification Training ?

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 is Machine Learning Engineering on AWS Certification Training for?

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.

What are the prerequisites for Machine Learning Engineering on AWS Certification Training?

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)

Available Training Modes

Live Online Training

Classroom Training

3 Days

Course Outline Expand All

Expand All

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

Introduction to ML

Amazon SageMaker AI

Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges

Evaluating ML business challenges

ML training approaches

ML training algorithms

Module 3: Data Processing for Machine Learning (ML)

Data preparation and types

Exploratory data analysis

AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering

Handling incorrect, duplicated, and missing data

Feature engineering concepts

Feature selection techniques

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

Amazon SageMaker AI built-in algorithms

Amazon SageMaker Autopilot

Selecting built-in training algorithms

Model selection considerations

Topic E: ML cost considerations

Module 6: Training Machine Learning (ML) Models

Model training concepts

Training models in Amazon SageMaker AI

Lab 3: Training a model with Amazon SageMaker AI

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

Evaluating model performance

Techniques to reduce training time

Hyperparameter tuning techniques

Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies

Deployment considerations and target options

Deployment strategies

Choosing a model inference strategy

Container and instance types for inference

Lab 5: Shifting Traffic

Module 9: Securing AWS Machine Learning (ML) Resources

Access control

Network access controls for ML resources

Security considerations for CI/CD pipelines

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

Introduction to MLOps

Automating testing in CI/CD pipelines

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

Detecting drift in ML models

SageMaker Model Monitor

Monitoring for data quality and model quality

Automated remediation and troubleshooting

Lab 7: Monitoring a Model for Data Drift

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Who is the instructor for this training?

The trainer for this Machine Learning Engineering on AWS Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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