Machine Learning Engineering on AWS Course
- Duration: 24 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
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Course Overview
The Machine Learning Engineering on AWS Course delivers comprehensive training for professionals responsible for designing and operating production-ready machine learning systems. Participants gain a structured understanding of how to engineer ML pipelines on AWS—from data ingestion and model training to deployment and monitoring. The course emphasizes scalability, reliability, and governance, helping organizations move from experimentation to enterprise-grade ML solutions with confidence.
Course Details
- Develop strong expertise in end-to-end machine learning engineering on AWS.
- Learn how to build, train, and deploy models using managed AWS services.
- Improve reliability and scalability of ML pipelines through MLOps practices.
- Strengthen understanding of model monitoring, versioning, and lifecycle management.
- Optimize performance, security, and cost of ML workloads.
- Support consistent delivery of production-ready machine learning solutions.
- Ideal for machine learning engineers, data scientists, and cloud engineers.
- Suitable for professionals responsible for deploying and operating ML solutions.
- Basic understanding of machine learning concepts recommended.
- Familiarity with Python and cloud computing concepts preferred.
- Explain machine learning engineering workflows on AWS.
- Prepare data and features for scalable ML training.
- Train, tune, and evaluate ML models using Amazon SageMaker.
- Deploy models using batch and real-time inference options.
- Implement monitoring, automation, and MLOps best practices.
- Operate secure, scalable ML solutions in production environments.
- Machine Learning Engineering Overview and AWS ML Services.
- Data Preparation and Feature Engineering.
- Model Training, Tuning, and Evaluation with Amazon SageMaker.
- Model Deployment and Inference Strategies.
- MLOps: Automation, CI/CD, and Model Lifecycle Management.
- Monitoring, Security, and Cost Optimization for ML Workloads.
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