Cloud Wizard - Machine Learning Engineering on AWS - 3 Days (MLA-C01)

Delivered by Cloud Wizard Consulting, an AWS Advanced Training Partner and Select Tier Consulting Partner, this intermediate course equips ML professionals with the skills to build, deploy, orchestrate, and operationalize machine learning solutions at scale on AWS. With over 5,000 professionals trained and a 100% certification pass rate, Cloud Wizard's AWS-certified instructors bring real-world consulting experience to every session.

• AWS Advanced Training Partner with Select Tier Consulting credentials
• 5,000+ professionals trained across organizations of all sizes
• 100% MLA-C01 pass rate among course graduates
• All instructors hold every AWS certification, ensuring deep, authoritative guidance
• Dedicated pre- and post-training support with 12-hour email response commitment

By the end of this course, participants will be able to:

• Explain ML fundamentals and applications in the AWS Cloud
• Process, transform, and engineer data for ML tasks using AWS services
• Select appropriate ML algorithms and modeling approaches based on problem requirements and interpretability
• Design and implement scalable ML pipelines using AWS services for training, deployment, and orchestration
• Create automated CI/CD pipelines for ML workflows
• Apply appropriate security measures for ML resources on AWS
• Implement monitoring strategies for deployed models, including data drift detection

Participants complete seven labs spanning the full ML lifecycle using Amazon SageMaker AI, Amazon EMR, and related services:

1. Analyze and prepare data with Amazon SageMaker Data Wrangler and Amazon EMR
2. Data processing using SageMaker Processing and the SageMaker Python SDK
3. Train a model with Amazon SageMaker AI
4. Model tuning and hyperparameter optimization with Amazon SageMaker AI
5. Shift traffic between model versions (A/B deployment) - simulating a real-time recommendation engine processing production traffic
6. Build ML pipelines using Amazon SageMaker Pipelines and Model Registry in SageMaker Studio
7. Monitor a model for data drift in production

In Lab 5, participants simulate shifting traffic between model versions for a real-time inference endpoint - a pattern used by retail teams personalizing recommendations and fintech teams deploying fraud-detection models. This exercise demonstrates canary and linear deployment strategies that reduce risk when updating production ML systems.

Lab environments are provisioned per participant and are ephemeral - destroyed after the course concludes. Participant enrollment data is handled in accordance with data protection best practices. All lab activity occurs within isolated AWS accounts to prevent cross-participant data exposure.

• DevOps Engineers
• SysOps Engineers
• Software Developers
• Solution Architects
• Individuals seeking the AWS Certified Machine Learning Engineer - Associate certification

• Familiarity with basic machine learning concepts
• Working knowledge of Python and common data science libraries (NumPy, Pandas, Scikit-learn)
• Basic understanding of cloud computing concepts and familiarity with AWS
• Experience with version control systems such as Git (beneficial but not required)

Module 0: Course Introduction | Module 1: Introduction to ML on AWS | Module 2: Analyzing ML Challenges | Module 3: Data Processing for ML | Module 4: Data Transformation and Feature Engineering | Module 5: Choosing a Modeling Approach | Module 6: Training ML Models | Module 7: Evaluating and Tuning ML Models | Module 8: Model Deployment Strategies | Module 9: Securing AWS ML Resources | Module 10: MLOps and Automated Deployment | Module 11: Monitoring Model Performance and Data Quality | Module 12: Course Wrap-up

This training prepares participants for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam while equipping them with practical skills to implement production-grade ML solutions on AWS.

To book a session or request a detailed syllabus, visit https://cloudwizardconsulting.com/aws-training/machine-learning-engineering-on-aws/  to schedule your cohort.