Engineering AI systems requires managing many integral artifacts. These include inputs such as datasets, configurations such as hyperparameters, and outputs such as training results. However, the methodological approach for such management is often unclear in practice. This training provides a practical introduction to AI engineering and shows how to systematically version and configure the aforementioned components of AI systems.

In a combination of short theory sessions and hands-on workshops, you will be able to try out open source technologies for standardized project structure, data versioning, and experiment tracking. There will also be the opportunity to discuss questions that arise and real problems from your company.

Contents of the training

• Comparison between developing 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 participants

• You will recognize the benefits, such as traceability, that come from versioning data, models, and code.
• You will be able to transfer and apply the workshop examples to your organization.

Registration

The workshop "Configuration Management for AI Engineering" (date 18.10.2022) can be booked directly at the following link.

Click here to register