Course English

Machine Learning Operations (MLOps)

Do you want to learn how to put Machine learning models like ChatGPT into production and thereby provide value to your company, using modern tools, processes, and practices? Then this course is for you.

While machine learning and AI seems like a magic tool to bring value to any company, the reality is that machine learning only brings value if it can be operationalized e.g., put into production. In this course we introduce the concept of Machine Learning Operations (MLOps), including the tools, practices and processes that are important to understand how modern machine learning models can be operationalized. This includes best practices from a software perspective, how to manage large models and datasets, and how to choose the right services/infrastructure for the different parts of the machine learning pipeline depending on requirements.

During the course/programme you will work with:
The theory and practical applications of the most prominent and common machine learning methodologies. Lectures will primarily cover the theories of the methods taught and exercises highlight their practical use and applications.

During the course/programme the customer will work with:

  • Basic software development practices for machine learning
  • How to use and operate large machine learning models (like LLMs)
  • Continues integration practices for machine learning
  • Cloud services for machine learning and how to link them together
  • Deployment of machine learning models, directly from trained or finetuned models to easy-to-use cloud services
  • Introduction to High Performance Computing as a secure alternative to commercial clouds for deployment of AI models

Who is the course relevant for?

The course is relevant for people with a general interest in machine learning but have not worked on operationalizing it e.g., deploying them to a production and continuously making sure they operate as expected. Participants should be comfortable with coding, as the course focuses on hands-on programming exercises to showcase tools and frameworks presented in the course.

What is in it for you?

  • A strong foundation of best practices within software development for creating modern machine learning pipelines.
  • Hands-on experience working with modern open-source machine learning models in PyTorch
  • Hands-on practice with technologies such as containers and continues integration.
  • Overview of different cloud services for working with the different steps of the machine learning pipeline.
  • The possibility for certification through report work, where the methods and tools taught in the course are applied to a problem of your choice.

What is in it for your company?

By having an employee who has participated in this course/programme the company has access to a person who can:

  • Implement and operate modern large ML models in Pytorch.
  • Apply software practices such as containers and continues integration to create reproducible, scalable, and maintainable ML models.
  • Identify advantages and disadvantages of AI models, services, tools, and processes relevant to operationalization.
  • Contribute to your company’s AI strategy and transform your company business based on operationalizing modern machine learning.

After completing the course, there is the option to work on a project within their own problem domain using the tools and methods taught throughout the course. This project should ideally be applied to problems of the company. Feedback on this project work will be provided to the participants. The participant will thereby, upon completing the course, be able to operationalize machine learning models that are relevant to the company.

Practical information


The course follows active learning approach, i.e., short lectures are followed by practical coding sessions on real life examples, supported by DTU professionals. Each day is divided into a morning session and an afternoon session. Each session consists of a 1-hour lecture followed by 2 hours of practical computer exercises.




Five days from Monday to Friday 9.00 – 16.00


On DTU Lyngby Campus in classes with up to 40 students

Teaching material

Teaching material will be accessible to all participants through an online webpage, that will include presentations given throughout the course, further reading material, practical exercises, and solutions.


In case of too few participants, we reserve the right to cancel or postpone the course.

Waiting list

In case of too many participants, we reserve the right to make a waiting list.

Admission requirements

Anyone interested in the operational part of doing modern machine learning.


  • Basic python programming skills
  • Knowledge about basic machine learning as covered in this course





DTU Lyngby Campus


5 days, from 9.00-16.00

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