MACHINE LEARNING · 5 WEEKS · 134 LESSONS
MLOps Platforms: Amazon SageMaker and Azure ML
Learn to implement end-to-end MLOps workflows using Amazon SageMaker and Azure ML services. Master the essential skills needed to build, deploy, and manage machine learning models in production environments across multiple cloud platforms.
Master the fundamentals of Machine Learning Operations (MLOps) using Amazon SageMaker and Azure ML services. This comprehensive course covers the entire ML lifecycle, from data engineering and exploratory analysis to model development, deployment, and operational management. Through hands-on labs and real-world scenarios, students will learn to build, train, and deploy machine learning models at scale while implementing MLOps best practices across both AWS and Azure cloud platforms.
01 — What learners say
Real feedback from real learners.
“The Rust Fundamentals course was fantastic for formalizing my base Rust knowledge. It was well-paced for both beginner and experienced developers.”
“The course was incredibly informative and well-structured. I gained practical skills that immediately applied in my work, making it a highly valuable learning experience.”
“The course on AI Fundamentals is not only engaging but also practical. Having worked with AWS before, the way it explained Azure cncepts made switching seamless and easy to grasp.”
“I can say that it's a great series of courses for those who are looking to practical MLOps knowledge.”
“This course got clear videos, exercises, and questions, and there are extra resources too. When you're done, you'll have a solid foundation to dive into topics like DevOps or ML.”
02 — Why this course
What you'll walk away with.
Master the fundamentals of Machine Learning Operations (MLOps) using Amazon SageMaker and Azure ML services. This comprehensive course covers the entire ML lifecycle, from data engineering and exploratory analysis to model development, deployment, and operational management. Through hands-on labs and real-world scenarios, students will learn to build, train, and deploy machine learning models at scale while implementing MLOps best practices across both AWS and Azure cloud platforms.
Design and implement end-to-end MLOps workflows using AWS SageMaker and Azure ML services
Build robust data engineering pipelines for machine learning model training and deployment
Develop and optimize machine learning models using cloud-native tools and services
Implement automated deployment strategies and monitoring solutions for ML models in production
03 — Syllabus
5 weeks, 134 lessons total.
- Meet your course instructor NOAH gift
- Meet your supporting instructor Alfredo DEZA
- Course structure and discussion Etiquette
- Getting started and course Gotchas
- Key terms
- Welcome to AWS academy machine learning foundations
- Key terms
- Aws academy introduction to machine learning
- Cleaning up data
- Scaling data
- Labeling data
- Aws resources for Exploratory data analysis
- Key terms
- When to use machine learning
- Supervised VS Unsupervised machine learning
- Introduction to implementing a machine learning pipeline with amazon Sagemaker
- Selecting a machine learning solution
- Lesson reflection
- Key terms
- Introducing natural language processing
- Monitoring and logging
- Interactive python logging
- Multiple regions
- Reproducible Workflows
- Key terms
- Introduction to Azure Certifications
- Learning resources for Azure Certifications
- Microsoft learning paths and study notes
- Creating an Azure ML Workspace
- Creating an Azure auto ML job
04 — For teams
Custom training for your company.
We're ready to deliver this and other courses to your team. We accommodate different requirements and are flexible with seat count.
- +Bulk pricing available
- +Customizable content
- +Ready to start on your schedule
05 — Individual access
Simple, straightforward pricing.
Full platform access — every current and future course — priced so cost isn't what decides whether you learn.
No commitment required. Cancel anytime.