Practical Crowdsourcing for ML at Scale

AI stands on three pillars: algorithms, hardware and training data. While the first two have already become commodities on the market, the latter - reliable labelled data - is still a bottleneck in the industry.
Need to add twice as much data to the training set to improve your model? Want to validate the accuracy of a new classificator in an hour? Or maybe you are building a human-in-the-loop process with 90% of cases processed automatically and the trickiest 10% of cases fine-tuned by people in real time. You can do it all with crowdsourcing, but only with crowdsourcing done right.
In this talk, we will discuss how the new generation of methods and tools allows to collect high quality human labelled data on a large scale and why every ML specialist should know how to use crowdsourcing.
You will learn from the talk:
* Understand the applicability, benefits and limits of the crowdsourcing approach.
* Integrate an on-demand workforce into your processes and build human-in-the-loop processes.
* Control the quality and accuracy of data labeling to develop high performing ML models.
* Understand the full-cycle crowdsourcing project
Speaker: Daria Baidakova(Toloka)
Slides: www.slideshare.net/BillLiu31/...