Vladimir is a lead machine learning engineer at AI Accelerator of Carl Zeiss AG, where he focuses on building and operating machine learning solutions and supervising the team of data scientists and ML engineers. His journey into the AI started with his Master’s Degree from Delft University, NL; was deepening during his PhD at the Karlsruhe Institute of Technology, DE and continued in the Industry including consulting and retail in energy sector where he was building end-to-end cloud ML systems. In his free time he geeks out with Raspberry PIs and enjoys hiking and travelling.
At Carl Zeiss AG, our goal is to offer responsible Machine Learning (ML/AI) applications our customers can trust. To build trust in ML/AI, an end-to-end solution to operate Machine Learning is imperative.
In this talk, we will show how our team is set up to develop state-of-the-art ML/AI applications. The success of these applications depends on the capability to manage the full ML/AI development and life-cycle management through a professional MLOps setup. We will share our journey towards sustainable ML/AI applications based on real use cases at Carl Zeiss AG. To build sustainable products that deliver real value, use cases need to go beyond proof-of-concept status, following a fully functional development, operationalization, and automation cycle. We believe in the power of standardization at the core and flexibility at the shell to develop reliable and reproducible ML/AI applications. MLOps drives this through the entire lifecycle of ML/AI models, from design to implementation to management. Our goal is to develop all necessary building blocks of a state-of-the-art MLOps solution to ensure fairness, accountability, and transparency.
 “ML Integrity: Four Production Pillars For Trustworthy AI” by Nisha Talagala Forbes Jan. 2019
 “Datasheets for Datasets” Kate Crawford et al.
 Keeping an eye on AI with Dr. Kate Crawford Microsoft Research Feb, 2018