Bio

Analytics and Data Engineer Leader with 10+ years of experience working in Business Intelligence, Data Warehouse & Data Integration, BigData, Cloud, and ML space across North America and Europe.

Leading Data Engineering initiatives, working on a petabyte-size data platform built using cloud and big data technologies for supporting machine learning experiments, data science models, business intelligence reporting, and data exchange with internal and external partners. Handling privacy compliance and security-critical data sets.
Apart from work, I am teaching a Cloud Computing course at the University of Victoria, mentoring high school students at CS faculty, and volunteering my time for coaching people with analytics engineering skills in the CIS region. Moreover, I am the author of analytics books and speaker at data-related conferences and user groups.

 

Data engineering challenges at ML projects on global scale solutions

Data Engineering & MLOps

Nowadays, companies choose to be data-driven, collect, store and explore their data. They start small from basic reporting and quickly realize the potential of data. They grow a team of data professionals with diverse skill sets, implement data platforms and self-service analytics. Eventually, they are mature enough to play with AI.Working in the data analytics space for the last decade with leading world companies such as Amazon and Microsoft, I engineered data platforms and helped businesses to unlock data potential. I faced lots of challenges and some of them more frequently than others. During my talk I want to share with you top challenges for data engineering and ML projects and their solutions.Hopefully, it will help you to avoid them in the future or at least you will get a clue what to expect and how to react.

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