At SuperAnnotate we truly believe that data is the source code for ML. If we consider all the systems, processes, tooling, expert help, guidance, that we get for building machine learning models, we need to start approaching building datasets with the same level of precision.
In this webinar, we’ll discuss some of the most common pitfalls Machine Learning teams face productionizing data pipelines and ways to avoid those issues. We’ll be covering:
At the end of this session, we hope you’ll have a better understanding of how to build SuperData at scale and in significantly less time.