Machine Learning Engineering on AWS: Build, Deploy and Operationalise Scalable ML Solutions
- Why get trained: Learn how to design ML workflows, process data, train models and deploy solutions using Amazon SageMaker, EMR and AWS ML services.
- Why it matters: End-to-end ML engineering skills help teams operationalise AI solutions, automate pipelines and deliver reliable, production-ready models.
- Who should attend: Machine learning engineers, data engineers, DevOps engineers and developers responsible for building and deploying ML solutions on AWS.
Build production-ready machine learning capability develop skills to design, deploy and manage scalable ML solutions. HRD Corp Claimable.
Overview
Develop production-ready machine learning applications on AWS with expert-led training.
93% of decision-makers say AWS Training is more relevant to project requirements than third-party options, highlighting the business impact of certified AWS ML professionals. (ESG Insights Paper: Understanding the Value of AWS Training to Organizations, January 2023)
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.
- learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities.
- 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
Skills Covered
- 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.
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)
Target Audience
- 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.
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
Dates & Locations
June 22, 2026 - June 24, 2026
June 22, 2026 - June 24, 2026
August 10, 2026 - August 12, 2026
August 10, 2026 - August 12, 2026
October 19, 2026 - October 21, 2026
October 19, 2026 - October 21, 2026
December 7, 2026 - December 9, 2026
December 7, 2026 - December 9, 2026
Exam & Certification
AWS Certified Machine Learning Engineer – Associate.
The AWS Certified Machine Learning Engineer – Associate certification validates technical ability in implementing ML workloads in production and operationalizing them. Boost your career profile and credibility, and position yourself for in-demand machine learning job roles.
Training & Certification Guide
Frequently Asked Questions
Speak to a Training Consultant
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Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
