MACHINE LEARNING · 5 WEEKS · 142 LESSONS
DevOps, DataOps, and MLOps
Learn to build and deploy production-ready machine learning systems using modern DevOps and MLOps practices. Master essential tools and frameworks while implementing end-to-end ML pipelines.
Master the intersection of DevOps, DataOps, and MLOps with this comprehensive course covering essential concepts and practical implementations. Learn to build and deploy machine learning pipelines, work with containers, implement CI/CD for ML projects, and develop end-to-end MLOps solutions using modern tools and frameworks like Rust, Python, and cloud services. The course combines theoretical knowledge with hands-on experience in building production-ready 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 intersection of DevOps, DataOps, and MLOps with this comprehensive course covering essential concepts and practical implementations. Learn to build and deploy machine learning pipelines, work with containers, implement CI/CD for ML projects, and develop end-to-end MLOps solutions using modern tools and frameworks like Rust, Python, and cloud services. The course combines theoretical knowledge with hands-on experience in building production-ready ML systems.
Understand core concepts and relationships between DevOps, DataOps, and MLOps methodologies
Build and deploy machine learning pipelines using modern tools and best practices
Implement containerization strategies for machine learning applications and microservices
Develop end-to-end MLOps solutions using languages like Python and Rust
03 — Syllabus
5 weeks, 142 lessons total.
- Getting started and course Gotchas
- Key terms
- Introduction to Mlops
- Mlops background
- Mlops trends and techniques
- What is Devops
- Key terms
- Doing data science your first day
- What is Colab
- Additional readings
- Quiz
- Lesson reflection
- Key terms
- Cloud developer Workspace advantage
- Key components of Github Ecosystem
- Using Github templates
- Demo of Github Codespaces
- Gpu code Whisperer
- Key terms
- Containerized Microservices
- Containerized continuous delivery
- Containerized machine learning
- Containerized end to end machine learning
- Building Distroless containers
- Key terms
- Introduction to switching to RUST from python
- Introduction to RUST lecture notes
- Configure RUST for AWS Cloud9
- Github Copilot enabled RUST programming
- Using RUST packaging for web development
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.