Machine Learning specialist with over 7 years of experience and curator of ML in Production course. He has worked with startups from idea to product development. He has experience in selecting and implementing modern deep learning architectures and large-scale solutions based on them. At the lecture, Kirill told what to do after deploying an ML model in production. What can go wrong afterwards, what to look for, and how to monitor your model. We understood not only the theoretical but also the practical part. We looked at such tools as Evident, Alibi Detect, Seldon Core, and understood how to use them with different domains. The speaker is Kyryl Truskovskyi. Machine Learning Engineer at Georgian, Co-Founder at ScoreInforce.