Two online machine learning workshops: Reinforcement Learning and MLOps

[ad_1]

Two exciting online training courses in the field of machine learning are on the program for the coming month of October.

Reinforcement learning shows its strengths wherever there is a need to learn the optimal strategy for solving tasks in a specific environment. Increasing computing power makes the formerly rather theoretical concept practicable.

The two-day online iX workshop Machine learning: reinforcement learning is aimed at developers and data scientists from companies and institutions and is limited to 15 participants. The training first teaches the theory behind the technology and thus helps in a fundamental understanding of the mechanisms of this type of machine learning. The following practical part uses relevant ML frameworks to show concrete examples how reinforcement learning works and for which application scenarios it is a suitable strategy.

The workshop will take place from October 5th to 7th, 2021. The trainer Dr. Arthur Varkentin works as a consultant in the fields of data science, AI and machine learning.

Artificial Intelligence – MLOps: Machine Learning: from the model to production is aimed at software developers, DevOps engineers and data scientists. In the new machine learning operations (MLOps) paradigm, software developers, DevOps engineers and data scientists work together and create a common basis for the successful commissioning of machine learning models in productive use.

How do you create a flexible, reliable ML pipeline? How can ML applications be used and managed on site, in the cloud or “on the edge”? What tools and test procedures are there? The two trainers Philipp Braunhart and Moustapha Karaki come from the field as machine learning engineers and answer all your questions in this workshop.

Spontaneous participants will receive a 10 percent discount on the workshop fee until September 27th.

More from iX magazine

More from iX magazine

More from iX magazine

More from iX magazine


(akl)

To home page

[ad_2]

Leave a Reply

Your email address will not be published. Required fields are marked *