Machine Learning Engineering on AWS (ILT)

In this course, participants learn to build, deploy, orchestrate, and operationalise 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.

Duration: 3 Days

What you'll learn:

• 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

Course Subjects:

• Introduction to Machine Learning (ML) on AWS
• Analysing 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

Target Audience:

This course is designed for professionals who are interested in building, deploying, and operationalising 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.

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