MACHINE LEARNING · 3 WEEKS · 55 LESSONS
Cloud Machine Learning Engineering and MLOps
Learn to build and deploy machine learning systems in cloud environments using modern MLOps practices and tools. Master essential skills in AutoML, continuous delivery, and edge computing while working with industry-standard platforms and frameworks.
Master the fundamentals of machine learning engineering and MLOps in cloud environments through comprehensive modules covering AutoML, continuous delivery, and edge computing. This course provides hands-on experience with various ML tools and platforms including Azure Machine Learning, Ludwig AutoML, and cloud-based APIs, while teaching essential concepts in ML engineering, microservices architecture, and automated deployment pipelines for production ML systems.
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 engineering and MLOps in cloud environments through comprehensive modules covering AutoML, continuous delivery, and edge computing. This course provides hands-on experience with various ML tools and platforms including Azure Machine Learning, Ludwig AutoML, and cloud-based APIs, while teaching essential concepts in ML engineering, microservices architecture, and automated deployment pipelines for production ML systems.
Understand and implement machine learning engineering practices for cloud environments using various AutoML platforms and tools
Design and deploy microservices-based ML systems with continuous delivery pipelines for production environments
Master edge machine learning deployment and integration with cloud services
Develop practical experience with Azure Machine Learning, Ludwig AutoML, and other cloud-based ML platforms
03 — Syllabus
3 weeks, 55 lessons total.
- Instructor introduction
- Course introduction
- Lab Onboarding
- Specialization project Roadmap course 4
- Project overview
- Course structure and discussion Etiquette
- Introduction to Automl
- What is Automl
- Automl computer vision
- No code low code Automl
- Apple create ML Automl
- Managed machine learning systems
- Introduction to Mlops
- What is Mlops
- Mlops deep dive
- Introduction to edge machine learning
- What is edge machine learning
- Edge machine learning in action
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.