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

Many organizations want to put machine learning into production, but ML projects often stall when teams lack AWS operations skills. The risks are real: SageMaker quick-setup defaults can create insecure configurations, workloads can end up overexposed to the internet, training costs can run unchecked, and models can introduce bias or drift.

For IT leaders: This course treats ML engineering mainly as MLOps, much of it DevOps and SysOps work applied to ML. That makes it a strong fit for DevOps engineers, system administrators, and cloud practitioners moving from on-prem operations to cloud AI projects. It also helps data scientists understand how AWS names, organizes, and runs its ML services.

• Risk and compliance: The course covers IAM, network isolation, data stewardship and compliance, and bias, explainability, and drift analysis.
• Cost control: Lessons cover SageMaker cost control and optimization considerations for ML workloads.
• Time investment: Plan for roughly 20 hours per learner across 20 skills. Topics move from the AWS ML ecosystem and cloud concepts into SageMaker setup, AWS ML workloads, and exam-style validation.
• Change-management note: Learners need an AWS account to use as an ML engineering lab. The instructor is candid that SageMaker version conflicts are normal, so schedule time for hands-on troubleshooting.

Exam-style validation questions in each skill, plus provided notebooks, help your team build practical machine learning engineering skills. AWS retired MLA-C01 on 28 September 2026 and now offers MLA-C02 for the same Associate credential.

IT teams use this training to move ML workloads from experiments to secure, cost-aware production on AWS. Instructor Scott Pletcher frames much of the work as MLOps, which sits close to DevOps and SysOps. That makes the course a practical fit for infrastructure and cloud staff who are taking on AI projects.

The course is grounded in scenarios your team will recognize. One example is a cautionary look at the SageMaker quick-setup button and the risk hidden in what it configures behind the scenes. Another covers dirty data inherited from dozens of purchased, homegrown, and legacy systems. The course also covers the version conflicts and documentation gaps that come with working in SageMaker. Learners see where vendor tutorials stop and real troubleshooting begins.

• Safer ML environments: Practitioners learn to secure SageMaker and related resources with IAM and network isolation. They learn to avoid quick-start defaults that can widen the attack surface.
• Cleaner data pipelines: Teams use Data Wrangler workflows to clean and transform messy source data. They also handle streaming ingestion and choose between options such as Kinesis and Apache Flink based on latency and scalability needs.
• Lower-risk model releases: Engineers compare deployment strategies such as shadow deployments, which validate a new model against live production traffic before cutover. They also automate workflows with SageMaker Pipelines and monitor for bias, explainability, and drift.
• Controlled cloud spend: Teams apply cost-optimization methods such as training on Spot instances and compiling models with SageMaker Neo. They learn to track long-running training jobs.

The result is a team that can choose the right ML approach for a given business problem, deploy with confidence, and work to the objectives AWS now measures with MLA-C02.

Knowledge

• How the AWS ML ecosystem fits together, including AWS terminology for teams coming from on-prem, other clouds, or pure data science backgrounds
• Where SageMaker quick-start defaults introduce hidden risk, and how IAM and network isolation reduce the attack surface of ML workloads
• How to choose a fitting ML approach for a business problem, from common supervised methods to sequence modeling
• Streaming ingestion trade-offs for ML workloads, including latency, managed scale, and intermittently connected IoT considerations
• Deployment strategies such as shadow deployments, plus methods for detecting bias, explainability gaps, and drift
• Cost levers for SageMaker training and deployment workflows
• Get hands-on with Amazon Bedrock, AWS's massive push into Generative AI

Ability

• Prepare and clean data with AWS Glue and Glue DataBrew, and augment image data with Python in JupyterLab notebooks
• Set up a SageMaker domain and work in SageMaker Studio, including experiment tracking with MLflow
• Train models with SageMaker built-in algorithms and JumpStart, and monitor training jobs and their logs
• Bring custom code and models into SageMaker with Script Mode, and work through version conflicts
• Build feature stores and automate ML workflows with SageMaker Pipelines
• Troubleshoot exam-style ML engineering scenarios drawn from the AWS Certified Machine Learning Engineer - Associate objectives