EP 139 - How to efficiently convert raw data into high-value training data for AI - Tigran Petrosyan, Co-Founder & CEO, SuperAnnotate
Data annotation is the hidden champion of machine learning. It is the process of tagging image, video, text, and other data in order to prepare it for training a model. The quality of your data annotation makes the difference between insight and noise.
In this week’s episode, we interview Tigran Petrosyan, co-founder and CEO of SuperAnnotate. We discuss how to manage and scale your annotation workflow, quickly spot quality issues in your data, and seamlessly integrate new data sets into your existing pipeline. We also explore how specialized agencies and AI are collaborating to accurately tag the high volume of data that AI training requires.
Key questions:
- How to manage the key steps of the annotation process - annotate, manage, automate, curate, and integrate?
- How can you deliver ML projects faster without compromising on quality?
- How should you balance the efforts of internal teams, freelancers, and automated tagging to achieve the right cost structure and performance?