MLOps Training

Machine Learning

During this training, we will cover the end-to-end process from notebook to production. You will learn how to apply MLOps principles such as continuous training, continuous deployment, and monitoring to build end-to-end solutions in one of the public clouds, like Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP). 

Looking to upskill your team(s) or organization? 

Niels will gladly help you further with custom training solutions for your organization.

Get in touch

What will you learn?

After the training, you will be able to:

Understand all the necessary components in an end-to-end ML system. 

Create machine learning pipelines in the cloud. 

Deploy your model as a scalable API 

Set up monitoring dashboards for your application. 

Integrate and deploy all code through a CI/CD pipeline. 

Key takeaways

  1. Key principles of MLOps.
  2. How to design solutions for deploying models to the cloud. 
  3. How to build an ML pipeline for reproducibly training a model.
  4. How to automatically deploy and schedule ML pipelines for automated re-training. 
  5. How to track models and metrics in a model library. 
  6. How to deploy and monitor an ML model as a scalable containerized REST API.  
  7. How to automate all components through CI/CD.
  8. Practical advice on how to put ML models into production in the cloud. 


This training will walk you through the deployment of machine-learning applications with one of the public cloud providers and automation processes with best engineering practices along the way. In a highly interactive environment, you will receive conceptual explanations and practical exercises from industry experts.

  • Key MLOps principles.
  • Creating a solution design.
  • Getting started with the cloud tooling. 
  • Training jobs and pipelines.
  • Experiment tracking. 

Who is it for?

This training course is perfect for software engineers who wish to learn more about Cloud and MLOps. And, who would like to incorporate best practices in development and software engineering.


Basic programming experience (Python, Git, Docker).

Software engineering skills (check, for example, our Python for Data Analysis course).

What else
should I know?

After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.

See you soon!

Course information

All literature and course materials are included in the price. 

After registering for this course, you will receive a confirmation email with practical information. 

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