One of the problems we faced when launching machine learning was difference in expertise. IT people are usually good at math and technology, business people normally deal with its outcomes and processes, but the key to success is in collaboration of these two areas.
I’m going to talk about our experience in pushing machine learning to business users and made it everyday technology: approaches, pitfalls, some issues and solutions, and tecnical side of a problem.
We would like to share with you our experience of creating and implementing a competency model for the employees of Grid Dynamics, a leading software engineering company. Our goal was to make a solution that automates the enterprise resource management process and, at the same time, helps our employees in their professional development and skills assessment.
We are going to present recently open sourced YoctoDB project — a small embedded Java-engine for extremely fast partitioned immutable-after-construction databases. We will briefly describe the architecture of indexing and search components, role and requirements on the search engine and our previous solution. Then we will dive into design and implementation of YoctoDB engine currently being used at Yandex.Auto and Auto.ru. In conclusion we will describe several Java pitfalls met along the…
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