MLOps on Azure
The Netherlands
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?
Rozaliia will gladly help you further with custom training solutions.
Rozaliia Khafizova
Data and AI 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)
Why should I do this training?
You will receive conceptual explanations and practical exercises from industry experts in a very interactive environment
Hands-on training with real life examples – learn today apply tomorrow!
Learn how to apply the best engineering practices to deploy machine learning applications on Azure
What else should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, the trainer will get in touch to ask you about any requirements you may have and any pre-course tasks you will need to do.
See you soon!
Training information
All literature and course materials are included in the price
Information on the software and tooling will be shared before the start date
This course requires a laptop
Online courses are delivered via Zoom or Microsoft Teams
Meet the Trainers
Jeroen Overschie
Jeroen is a Machine Learning Engineer at Xebia Data. Jeroen has a background in Software Engineering and Data Science and helps companies take their Machine Learning solutions into production. Jeroen has worked extensively with all things Data Science, MLOps, GenAI/LLM’s and knows his way around tooling to deliver production-quality Python code.
Jetze Schuurmans
Jetze is a well-rounded Machine Learning Engineer who is as comfortable solving Data Science use cases as he is productionizing them in the cloud. His interests include MLOps, GenAI and Cloud Engineering.
Yke Rusticus
Yke is an experienced machine learning engineer who has helped various companies generate value from their machine learning solutions. As trainer, his goal is to make sure you learn how to get your solutions beyond the experimentation phase, and into the real world.
Roy van Santen
Roy is a machine learning engineer interested in the latest technologies who will always use the tool that best suits the job. He goes by the motto: “if your model is not running in production, it does not exist”. It is better to put a model in production that delivers some value, than having none at all. Production is where the fun happens!
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