Role of Automation in ML DataOps

Integrating intelligent automation across machine learning data operations simplifies data preparation, optimizes data workflows and saves significant time and resources. In this session, Danny Lange, SVP of AI and Machine Learning at Unity reveals the role automation plays in machine learning data operations today, and in the foreseeable future.

Watch this session to gain insights on: 

  • Evolution of the ML DataOps landscape
  • Limitations in the current MLOps ecosystem and how automation can help
  • Impact of automation on Unity’s data workflows
  • Integrating human intelligence and processes to achieve high-quality data

Here are 3 key takeaways from the session:

  • In the early days of the machine learning industry, efforts and research were mainly focused on models and theory. Today it’s less about the details of the model, and more about scalability and deployment and focus on getting the right data.
  • As Unity saw an increase over the past few years from 500-700 million users to 3-4 billion users, they had to define an automation process to capture all the play data, process it, and send it back to the gaming studios for use cases such as in-game monetization, app monetization or changes to the games themselves.
  • With the changes brought by automation, organizations need to make upgrades to teams and make team members really understand how their skills need to be scaled up and how they can grow.

Ready to learn how iMerit can help with your machine learning data operations?