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Machine Learning Engineering on AWS

Name: Machine Learning Engineering on AWS
Availability: InStock
Rating: 4.6 (5878 reviews)

4.6 out of 5 rating
Last updated : 23/07/2026
English

Available as Instructor Led Training, Live Online & In Person at your Offices or Ours.

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Download Overview

Duration:

24.00 hours

19.5 CPD hours

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.

Description:

- Course Introduction
- Introduction to Machine Learning (ML) on AWS
- Analyzing Machine Learning (ML) Challenges
- Data Processing for Machine Learning (ML)
- Data Transformation and Feature Engineering
- Choosing a Modeling Approach
- Training Machine Learning (ML) Models
- Evaluating and Tuning Machine Learning (ML) models
- Model Deployment Strategies
- Securing AWS Machine Learning (ML) Resources
- Machine Learning Operations (MLOps) and Automated Deployment
- Monitoring Model Performance and Data Quality
- Course Wrap-up

Course Outline:

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: 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
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, Course Introduction, Introduction to Machine Learning (ML) on AWS, Analyzing Machine Learning (ML) Challenges, Data Processing for Machine Learning (ML), Data Transformation and Feature Engineering, Choosing a Modeling Approach, Training Machine Learning (ML) Models, Evaluating and Tuning Machine Learning (ML) models, Model Deployment Strategies, Securing AWS Machine Learning (ML) Resources, Machine Learning Operations (MLOps) and Automated Deployment, Monitoring Model Performance and Data Quality, Course Wrap-up

Available Delivery Options for Machine Learning Engineering on AWS

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Machine Learning Engineering on AWS provide?

The 3 day Machine Learning Engineering on AWS course gives you up to 19.5 CPD hours/structured learning hours.

Who is the correct audience for Machine Learning Engineering on AWS?

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.

Do you provide training for Machine Learning Engineering on AWS?

Yes, we provide corporate training, dedicated training and closed classes for Machine Learning Engineering on AWS. This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Machine Learning Engineering on AWS?

The Machine Learning Engineering on AWS training takes place over 3 day(s), with each day lasting approximately 24.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Machine Learning Engineering on AWS, Machine Learning, MLEA, Amazon Web Services, vILT

Why are Nexus Human the best provider for Machine Learning Engineering on AWS?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Machine Learning Engineering on AWS?

Yes, the discount code PENPAL5 is currently available for Machine Learning Engineering on AWS training. Only one discount code can be used per booking.

DURATION 1 Day

No scheduled classes available at this time.

Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable.

To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.