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
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
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.
Course Curriculum
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
Dates & Locations
October 19, 2026 - October 21, 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.
