Best practices for the development and management of AI projects
The engineering of AI systems requires the management of many integral artifacts, for example inputs such as data sets, configurations such as hyperparameters and outputs such as training results. However, the methodological approach for such management is often unclear in practice. In this webinar, fortiss scientist Alexandros Tsakpinis 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. The webinar will be held as part of the Mittelstand-Digital Zentrum Augsburg.
Within short theoretical units, you can get to know methods for standardized project structure, data versioning and experiment tracking. You will also have the opportunity to discuss questions and real problems from your company.
The webinar is targeted at technical employees (e.g. data scientists, ML engineers, software engineers, product managers).
Knowledge of Python, including initial prototypes for your own AI systems, is required for participation.
