The engineering of AI systems requires the management of many integral artifacts. These include inputs such as data sets, configurations such as hyperparameters and outputs such as training results. However, the methodical approach for such management is often unclear in practice.

In this course, Alexandros Tsakpinis from the fortiss Center for Code Excellence (CCE) will give you an introduction to AI engineering - moving away from the prototypical environment within a Jupyter notebook - and show you how the aforementioned components of AI systems can be systematically versioned and configured.

In a combination of short theory units and live coding sessions, you can get to know open source technologies for a standardized project structure, data versioning and experiment tracking. There will also be the opportunity to discuss questions and real problems from your company.
 

Content

• Comparison between the development of an AI prototype in Jupyter Notebook and a standardized project structure
• Methods for data versioning and experiment tracking
• Overview of open source technologies for a standardized project structure, data versioning and experiment tracking
   

Benefits for the participants

• They recognize the advantages, such as traceability, that arise from versioning data, models and code
• You can transfer and apply the examples from the live coding sessions to your company

Registration

Register now for the training free of charge!

Register now!

This is a follow-up training to the webinar “Best Practices for Developing and Managing AI Projects” on November 6.

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