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
Target group
The event is addressed to technical employees (e.g. data scientists, ML engineers, software engineers, product managers).
Prerequisites
Important: Knowledge of Python, including initial prototypes for your own AI systems, is required for the training. Participation in the associated webinar is not mandatory.
More information
Center for Code Excellence (CCE)
Mittelstand Digital Zentrum Augsburg
The training will be held as part of the Mittelstand-Digital Zentrum Augsburg.
