Course Overview:

Industrial systems’ effectiveness is paramount. However, conventional industrial equipment monitoring and control approaches are inefficient when it comes to the optimization of complex technological processes or systems. Optimization dilemma with multiple variables and several optimization objectives (e.g., reducing production time while maintaining quality and maintenance cost at the appropriate level) can be solved only with computational methods. Humans can typically solve problems with up to 20 variables and tradeoffs but machines can solve much more complex optimization problems with a virtually unlimited number of variables and deliver tremendous outcomes to maximize effectiveness across the entire value chain. During this session, Iurii Milovanov, SoftServe’s AI leader, will overview best practices in using AI and Data Science technologies powered by telemetry and data from MES and ERP systems to derive the most optimal configuration of the production line. Iurii will also share his design recommendations on building large-scale AI systems that leverage the full power of Cloud, IoT, and ML infrastructure using practical examples and real-life use cases.

Presenter Bio:

Iurii Milovanov, SoftServe’s Director of AI and Data Science – 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.