AWS Certified Machine Learning Engineer – Associate (MLA-C02)– Beta

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• Unlimited access for 2 years

• Course completion certificate

• 2 practice tests with 130+ unique questions and exhaustive explanations

• Course crafted by industry experts

Machine learning engineering has expanded beyond traditional model development to include generative AI, foundation models, LLMs, and agentic AI applications. AWS has updated the MLA certification to reflect these evolving responsibilities while retaining its core ML engineering focus.

This course provides a structured learning path aligned with the MLA-C02 exam domains, helping learners strengthen their understanding of data preparation, model development, deployment, orchestration, monitoring, security, and production operations.

Whether you work with traditional ML models, generative AI applications, or production ML pipelines, this course helps you build the skills required to implement and operationalize AI and ML solutions using AWS.

Domain 1: Data Preparation for ML and AI — 28%

Learn how to ingest, transform, validate, and prepare data for machine learning and AI workloads. This domain covers data processing, feature engineering, data quality, storage, and preparation techniques required for ML and AI solutions.

Domain 2: ML Model and Foundation Model (FM) Development — 24%

Understand how to select modeling approaches, train and tune ML models, analyze model performance, manage model versions, and work with foundation models and generative AI workloads.

Domain 3: Deployment and Orchestration of ML and AI Workflows — 24%

Learn how to select deployment infrastructure, configure endpoints and compute resources, implement scaling, and establish CI/CD pipelines for ML and AI workflows.

Domain 4: Operating, Monitoring, and Securing ML and AI Solutions — 24%

Explore how to monitor models, AI workflows, data, and infrastructure while applying security controls, compliance practices, observability, and operational techniques for production ML and AI solutions.

To get the most from this course, learners should have:

• At least 1 year of experience using Amazon SageMaker AI, Amazon Bedrock, and other AWS services for ML engineering

• At least 1 year of experience in a related role such as ML engineer, MLOps engineer, data engineer, backend developer, or data scientist

• Experience working with both traditional machine learning and generative AI

• Understanding of common ML algorithms and their use cases

• There are no mandatory prerequisites for taking this course.

• Practical AWS experience with machine learning services is strongly recommended.

• Familiarity with Amazon SageMaker AI and Amazon Bedrock is beneficial.

• Basic understanding of machine learning, generative AI, foundation models, and data engineering is recommended.

The MLA-C02 beta exam introduces an updated scope that reflects the evolving role of machine learning engineers. The beta includes coverage of:

• Generative AI implementation

• Amazon Bedrock

• Retrieval-Augmented Generation (RAG)

• Foundation models and LLMs

• Agentic AI workflows

By completing this course, you will be able to:

• Prepare and transform data for ML and AI workloads.

• Apply data validation and feature engineering techniques.

• Select appropriate ML modeling approaches.

• Train, tune, evaluate, and manage ML models.

• Work with foundation models and generative AI workloads.

This course is ideal for:

• Machine Learning Engineers

• MLOps Engineers

• LLMOps Engineers

• Data Engineers

• Data Scientists

After completing this course and earning the certification, learners can apply their skills in roles such as:

• Machine Learning Engineer

• MLOps Engineer

• LLMOps Engineer

• AI/ML Engineer

• Data Engineer

• Prepare for the AWS Certified Machine Learning Engineer – Associate (MLA-C02) exam.

• Build practical skills for operationalizing ML workloads on AWS.

• Develop knowledge of both traditional ML and generative AI.

• Learn how to work with foundation models and LLMs.

• Understand Amazon Bedrock and RAG architectures.

• Data Preparation for ML and AI

• Feature Engineering

• Machine Learning Model Development

• Foundation Model Development

• Generative AI