Data Science Meet-up

📅 Book your calendars for Data Science Meet-up and get a large dose of knowledge by joining not one, but two lectures prepared by our experts.
✔️Check out the topics:
Talk#1 Oleh Lokshyn, Machine Learning Architect “MLOps: why should I care?”
Taking an ML solution to production can be a challenging task, and keeping the model performing well over extended periods of time can sometimes be even harder. During the presentation you will learn about:
🔹 What is the lifecycle of an ML project?
🔹 How to take control of this lifecycle and make it efficient?
🔹 How to ensure the quality of your data and models over time and not disappoint your users?
🔹 How to automate the parts of the ML process and give your ML professionals more freedom and flexibility?
Talk#2 Yaroslav Svyryda, Data Scientist “Data Science in Practice – latest trends and case study”
It’s no secret that market's perception of what Data Scientist is capable of is pretty broad and changes over time. This talk will help you to find out more about:
🔹 Data Science Group (DSG), its role, recent achievements and mission,
🔹 Latest trends we see on the market and how DSG reacts to it,
🔹 Public case studies, where DSG played critical role.

⚡ Register now and don’t miss your chance to join ⚡

* - обов'язкові поля
Час початку:
20.05.2021 17:00
Час завершення:
20.05.2021 19:00
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Онлайн вебінар:
деталі будуть надіслані у квитку
✔️ Meet the speakers:
Oleh Lokshyn, Machine Learning Architect at SoftServe. He built ML workflows on GCP, Azure, and on-premises for different supervised and unsupervised models. Oleh holds several certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Data Engineer, Microsoft Certified Azure Data Scientist Associate.

Yaroslav Svyryda, Data Scientist at SoftServe with more than 3 years of experience in the field. He studied quantitative methods in economics and information systems at the Warsaw School of Economics and has experience working on analytical topics including price optimization, demand forecasting, NLP, predictive maintenance and anomaly detection. One of his hobbies is the application of reinforcement learning in optimizing transportation systems.