DevOps meets Data Science - how to prepare?

Big data, data science, machine learning is coming to a lot of companies. Everyone is used to the creation of ordinary software, but BD/DS/ML requires special care. Managers and developers may get unfamiliar problems and I want to tell you about them and solutions - no money and nerves should be wasted.

Everyone has heard of data science, machine learning and big data. Many companies are starting to build up teams and run projects. Everyone knows how to develop, deliver and deploy ordinary software, but data-driven software is a different animal. Scientists, developers and managers may not be familiar with the issues that may come up.




Mikhail Iljin

Data Scientist / Big Data Developer at Mooncascade

The field of big data and data science Mike's huge passion and he have seen it develop in Estonia from "What are you, some scam artists?" to wide recognition among serious companies. During his everyday work with a variety of customers, he has seen, unfortunately, a lot of failures, but also eventual successes - and would love to share his experience with you.

He has 8 years of experience in software engineering (Java, Python, Oracle, Hadoop etc). Been involved in projects for the famous e-Government of Estonia, also been building the brand new e-ID/digital signature system that now serves every Estonian resident. Later, during the last 3 years he has been dealing with big data, data science, machine learning - mostly from the engineering perspective, but from the business development side too, as this area is still young and immature. Also, for a year been doing his own machine learning-related startup.