MLOps Engineering on AWS Course
- Duration: 24 Hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The MLOps Engineering on AWS Course equips technical professionals with the knowledge and skills to implement and manage machine learning operations on the AWS platform. The curriculum blends principles of DevOps and MLOps, focusing on full ML workflow automation, model training, deployment to production, and operational monitoring. Through demonstrations and labs using Amazon SageMaker, Airflow, Kubernetes, and AWS automation tools, learners gain real‑world proficiency in building scalable ML operations pipelines.
Course Details
- Grasp MLOps fundamentals and distinguish DevOps from MLOps practices
- Build automated ML pipelines for model build, test, and deployment
- Gain hands on experience with AWS SageMaker and orchestration tools
- Learn deployment strategies including A/B testing and edge scenarios
- Understand monitoring and governance practices for production ML systems
- Develop operational excellence in ML lifecycle automation and CI/CD
- Ideal for ML engineers, DevOps engineers, and professionals responsible for productionizing ML workloads
- Recommended prerequisites include AWS Technical Essentials and DevOps Engineering on AWS, or equivalent experience
- Experience with SageMaker or data science workflows is beneficial
- Define machine learning operations and MLOps goals
- Explain differences between DevOps and MLOps approaches
- Map out end to end ML workflows and automation options
- Leverage Amazon SageMaker for pipeline automation and deployment
- Package, deploy, and serve ML models with production variants
- Implement monitoring with drift detection and performance tracking
- Apply security and governance best practices to ML systems
- MLOps principles, goals, and lifecycle
- ML workflow automation and DevOps integration
- Security and governance in MLOps
- SageMaker integration for model build and orchestration
- Model deployment strategies and inference patterns
- CI/CD for ML systems and workflow orchestration tools
- Monitoring, drift detection, and operational metrics
- Hands on labs and practical demonstrations
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