Deliver an ML Solution in Days with AWS SageMaker

DURATION:

53 min

FORMAT:

Demo,Presentation

Best practices in building large-scale ML systems on Amazon SageMaker

In this session, SoftServe will share design recommendations and best practices in building large-scale machine learning systems on Amazon SageMaker using practical examples and real-life use cases. You will learn how to address modern business and technical challenges as well as how to bridge the gap between data, science, IT, business stakeholders, and end-users.

About speakers:

Iurii Milovanov

Iurii Milovanov is a Data Science Practice Leader with more than 10 years of experience in building enterprise-level AI, big data and advanced analytics solutions. Iurii is a computer science expert with strong emphasis on cutting-edge technologies. His research interests include various aspects of modern, progressive IT, and state-of-the-art artificial intelligence, such as distributed and parallel computing, large-scale machine learning, natural language processing, computer vision, and speech recognition. Iurii is actively contributing to various research and scientific communities, including his participation in the KarooGP project, a genetic programming suite used at LIGO Lab for detecting gravitational-waves; SIMOC, an interactive model of a scalable, human community located on a remote planet; and DRLearner project, the first open source implementation of Google’s Deep Reinforcement Learning (DQN) algorithm for playing ATARI games.