If you’ve ever spent way too much time dealing with messy experiments (I did in the past too) and wondering why your results are all over the place, you know how frustrating it can be. It’s easy to get lost in the chaos of managing data, tracking models, and ensuring that your experiments are reproducible. But what if you could finally take control?

In this session, we’ll show you how to bring order to your data science workflows with DVC (Data Version Control). With DVC, you can transform your chaotic workflows into something organized and easy to repeat. Whether you’re working solo or as part of a team, DVC helps you manage your data, models, and experiment results with ease.

After attending this talk you will understand how to:
- Bring in the right data, getting a clear view of what you’re working with and keeping everything organized.
- Track versions of your data and models, so you never have to wonder which version you used. DVC takes care of the details for you.
- Run experiments with confidence, knowing that if you re-run your pipeline you’ll get the same results every time.
- Build workflows that aren’t just functional but crystal clear and reliable.