MLOps on Azure
Do you already have a solid understanding of Machine Learning and would like to take your models outside of the development phase? This MLOps on Azure training is then a perfect next step for you.
Learn more about the key principles of MLOps and the elements of an end-to-end Machine Learning system. Also, you will create Machine-Learning pipelines in Azure ML and deploy a CI/CD pipeline with GitHub Actions. And much more!
Looking to upskill your team(s) or organization?
Surat will gladly help you further with custom training solutions.
Surat Wahal
Training Advisor
What will you learn?
After the training, you will be able to:
Understand all the necessary components in an end-to-end ML system
Set up monitoring for your application using Application Insights
Create machine learning pipelines in Azure ML
Integrate and deploy all code through a CI/CD pipeline with Github Actions
Deploy your model as scalable API as Azure ML Endpoint or Azure Container Instance
Program
- Discover key MLOps principles
- Creating a solution design
- Data versioning, environments and compute targets
- ML training jobs
- Experiment tracking
This training is for you if:
You already have a solid understanding of ML, and want to take your models outside of the development phase
You want to incorporate best practices from Software Engineering
You already have foundational software engineering skills (Python, Git, Docker)
You want to learn more about the cloud and MLOps
This training is not for you if:
You want to learn how to develop ML models (check out the Certified Data Science with Python or the Advanced Data Science with Python trainings)
You do not have basic programming experience (check out our Python for Data Analyst course)
You want to learn general methods for developing production-ready applications without focusing on a specific public cloud (check out our Production-Ready Machine Learning course)
Your primary interest is in (exploratory) research; this course is geared towards ML engineering
You are interested in a different cloud service (check out our MLOps on GCP or our MLOps on AWS training)
