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Artificial Intelligence and Machine Learning
AWS Machine Learning Engineer – Associate
Build, deploy and operate ML and AI solutions on AWS (MLA-C02)
A lab-based classroom course in putting machine learning into production on AWS. It follows the four domains of the official MLA-C02 exam guide: data preparation for ML and AI; ML model and foundation model development; deployment and orchestration of ML and AI workflows; and operating, monitoring and securing ML and AI solutions.
10Modules
IntermediateLevel
4 monthsDuration
COURSE THUMBNAIL
What you will be able to do
Ingest, transform and prepare data for modelling
Train, tune and evaluate models and manage their versions
Deploy models to endpoints that scale
Automate ML workflows with CI/CD pipelines
Monitor and secure ML and AI solutions in production
Sit the MLA-C02 exam having covered every domain of its guide
Course outline
01
Ingesting and storing data for ML
02
Transforming, validating and preparing data
03
Choosing a modelling approach and training models
04
Hyperparameter tuning, evaluation and model versions
05
Working with foundation models on Amazon Bedrock
06
Deployment infrastructure, endpoints and auto scaling
07
CI/CD pipelines and orchestration for ML workflows
08
Agentic workflows and observability
09
Monitoring models, data and infrastructure
10
Securing ML systems; exam practice
Tools you will use
Amazon SageMaker AIAmazon BedrockAmazon S3AWS GlueAmazon CloudWatchAWS IAMPython
What you need
Working knowledge of Python and common machine learning algorithms
Basic experience with AWS
