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 across 9 modules
- 13 quizzes with instant feedback
- 6 cloud labs with no setup required
- AI Tutor on every lesson
- Architect Production-Ready ML SystemsDesign and implement resilient machine learning systems using Amazon SageMaker, ensuring scalability and governance in production environments.
- Optimize Model Training and PerformanceApply hyperparameter tuning and automated model building techniques to enhance model accuracy and efficiency in real-world applications.
- Deploy and Monitor ML Models EffectivelyManage model deployment strategies, including real-time and batch inference, while ensuring continuous monitoring and compliance.
- Implement MLOps Best PracticesEstablish CI/CD pipelines and governance frameworks that streamline the machine learning lifecycle and enhance operational efficiency.
Naeem ul Haq is the co-founder and CTO of Educative, a hands-on learning platform for software engineers. Before co-founding Educative, he spent seven years as a software engineer at Microsoft, where he worked on the Windows Azure Fabric Controller, a core component of what became Microsoft Azure, and on Remote Desktop for Windows Server. Naeem co-founded Educative in 2015 alongside his brother Fahim to make developer education more interactive and accessible. His courses on Educative cover cloud technologies and full-stack web development, drawing directly from his experience building production infrastructure at Microsoft.
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What You'll Learn
- 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.
Learning Roadmap
1.
Introduction
Introduction
2.
Foundations and AWS Ecosystem
Foundations and AWS Ecosystem
3.
Data Preparation and Feature Engineering
Data Preparation and Feature Engineering
5 Lessons
5 Lessons
4.
Model Training and Optimization
Model Training and Optimization
4 Lessons
4 Lessons
5.
Generative AI and Advanced Compute
Generative AI and Advanced Compute
3 Lessons
3 Lessons
6.
Deployment and Inference
Deployment and Inference
4 Lessons
4 Lessons
7.
MLOps and Automation
MLOps and Automation
4 Lessons
4 Lessons
8.
Monitoring and Governance in ML Systems
Monitoring and Governance in ML Systems
3 Lessons
3 Lessons
Naeem ul Haq
Educative co-founder and CTO. Ex-Microsoft (Azure). Full-Stack, Cloud, Product & Engineering Leadership.
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