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Mastering Amazon SageMaker AI: Zero to Production MLOps
Lead the GenAI revolution with Amazon SageMaker. Future-proof your skills by learning to build, train, and deploy machine learning models from scratch to production-ready MLOps.
29 Lessons
6 Cloud Labs
5h
Updated today
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- Establish a secure Amazon SageMaker environment for machine learning projects.
- Implement data preparation and feature engineering techniques within the ML lifecycle.
- Train and optimize machine learning models using hyperparameter tuning and automated model building.
- Deploy and manage models in production with various inference patterns and governance strategies.
- Design and implement MLOps pipelines for continuous integration and delivery of machine learning models.
- Evaluate model performance and ensure compliance through observability and bias detection techniques.
KEY OUTCOMES
Architect Production-Ready ML Systems
Design and implement resilient machine learning systems using Amazon SageMaker, ensuring scalability and governance in production environments.
Optimize Model Training and Performance
Apply hyperparameter tuning and automated model building techniques to enhance model accuracy and efficiency in real-world applications.
Deploy and Monitor ML Models Effectively
Manage model deployment strategies, including real-time and batch inference, while ensuring continuous monitoring and compliance.
Implement MLOps Best Practices
Establish CI/CD pipelines and governance frameworks that streamline the machine learning lifecycle and enhance operational efficiency.
Why choose this course?
Stay Relevant in a Rapidly Evolving Field
In today's fast-paced tech landscape, the fear of being left behind in machine learning is real. Without mastering MLOps, your skills may quickly become obsolete, jeopardizing your career and opportunities.
The Challenges of Machine Learning Deployment
Even skilled developers struggle with the complexities of deploying machine learning models. Failing to bridge the gap between experimentation and production can lead to wasted resources and missed business goals.
Master MLOps with Hands-On Learning
This course offers a practical, project-based approach to mastering Amazon SageMaker. You'll gain hands-on experience in building, deploying, and managing production-ready ML systems, ensuring you are job-ready and competitive.
Elevate Your Career Today
Join a community of forward-thinking professionals and take the next step in your career. Equip yourself with the skills to excel in MLOps and make a meaningful impact in the field of machine learning.
Learning Roadmap
1.
Introduction
Introduction
Master end-to-end ML system design and deployment using Amazon SageMaker AI.
2.
Foundations and AWS Ecosystem
Foundations and AWS Ecosystem
Master resilient ML systems using Amazon SageMaker for production-ready workflows.
3.
Data Preparation and Feature Engineering
Data Preparation and Feature Engineering
5 Lessons
5 Lessons
Master scalable data processing and governance for efficient machine learning workflows.
4.
Model Training and Optimization
Model Training and Optimization
4 Lessons
4 Lessons
Explore efficient model training, automation, and scaling techniques in SageMaker AI.
5.
Generative AI and Advanced Compute
Generative AI and Advanced Compute
3 Lessons
3 Lessons
Explore advanced model deployment and training strategies using SageMaker for effective AI solutions.
6.
Deployment and Inference
Deployment and Inference
4 Lessons
4 Lessons
Master high-availability inference architecture and optimize deployment strategies in SageMaker.
7.
MLOps and Automation
MLOps and Automation
4 Lessons
4 Lessons
Master operationalizing ML with SageMaker Pipelines, experiment tracking, and hub-and-spoke strategies.
8.
Monitoring and Governance in ML Systems
Monitoring and Governance in ML Systems
3 Lessons
3 Lessons
Ensure model accuracy and fairness through observability and bias detection in ML systems.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
Amazon SageMaker has become the foundation of modern AI development on AWS, powering everything from model training to deployment and MLOps automation. As companies move AI systems into production, engineers are expected to understand not just how to build models, but also how to operationalize them reliably at scale.
I created this course from my experience leading engineering teams and developing scalable learning infrastructure. One pattern I repeatedly saw was that many engineers could train models, but struggled to deploy, monitor, and automate them in production environments. This course was designed to close that gap.
You’ll establish secure SageMaker environments, prepare datasets, train and optimize models, deploy inference endpoints, and build production-ready MLOps pipelines. The course also covers foundation models with SageMaker JumpStart, distributed training with HyperPod, CI/CD automation, drift detection, and bias monitoring.
If you want practical experience building AI systems on AWS with Amazon SageMaker, this course is an excellent place to start.
ABOUT THE AUTHOR
Naeem ul Haq
Educative co-founder and CTO. Ex-Microsoft (Azure). Full-Stack, Cloud, Product & Engineering Leadership.
Trusted by 3 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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