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Human managed data prep: Pachyderm + Toloka
Data preparation has always been a tedious and lengthy process for ML and AI. We discussed how to architect a Human in the Loop system to build a more ethical and better stack.
Overview
Data preparation has always been a tedious and lengthy process for machine learning and artificial intelligence. As teams look to automate this part of the machine learning lifecycle, they must still handle challenges to categorizing and labeling their data. But by using a combination of crowdsourced data labeling and automation, teams can augment their ML capabilities.
In this webinar attendees learned:- What is the Machine Learning Lifecycle
- Why it’s important to integrate human oversight into ML
- How you can use a combination of automation and human judgement for a winning stack
Speakers
Magdalena Konkiewicz
TolokaMagdalena holds a Master's degree in Artificial Intelligence from Edinburgh University. She’s worked as an NLP engineer, developer, and data scientist for businesses in Europe and America. She now teaches and mentors Data Scientists, and regularly contributes to publications like Towards Data Science.Profile linkJimmy Whitaker
PachydermJimmy Whitaker is the Data Science Evangelist at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.”Profile linkRecording
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