Master SageMaker, MLOps, pipelines & deployment. Build real ML systems & pass AWS ML Engineer Associate
Sub Category
- Data Science
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Objectives
- Build end-to-end machine learning pipelines on AWS using services like S3, Glue, Athena, and SageMaker
- Train, tune, and deploy ML models using SageMaker, including hyperparameter tuning and real-time inference
- Design scalable, production-ready ML architectures for real-world use cases such as recommendation systems and fraud detection
- Implement MLOps practices including pipelines, automation, monitoring, and model retraining strategies
- Understand feature engineering, data preprocessing, and how to use SageMaker Feature Store effectively
- Apply best practices for model evaluation, bias-variance tradeoff, and performance optimization
- Secure ML systems using IAM, encryption, and governance best practices on AWS
- Prepare confidently for the AWS Machine Learning Engineer Associate certification with real exam-style questions
Pre Requisites
- Basic understanding of Python (variables, functions, and simple scripts)
- Familiarity with fundamental concepts of machine learning is helpful but not required
- No prior AWS experience needed — everything will be taught from scratch
- A computer with internet access to run labs and access AWS services
- Willingness to learn by building real-world projects and completing hands-on exercises
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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