Scalable Machine Learning Platform
Video
Some system keeping data from different sources in high quality and in broad accessibility is the key to any successful application of machine learning. We introduce architecture of such system currently in place in Avast and discuss motivations and key decisions in its design.
We further share various lessons learned on how to: build and operate the system that serves the needs of human data analyst, supports automated real-time decision making by machines, and makes it easy to learn and deploy new ML models.
Jan Strnad
Software Developer
Avast
Honza graduated with a degree in computer science and since then has worked at Avast, where he primarily develops backend applications. His interests include functional programming and programming in general. Lately, he's been especially interested in big data.
Ondřej Zahradník
Cloud Architecture Director
Avast Software
Ondra graduated with a degree in computer science and led a team of Android developers, helping to build the mobile apps development studio Inmite, which was acquired by Avast. He focuses on market segmentation, business intelligence, and creating tools that support Avast sales folks using machine learning.