science – Science

Analyzing COVID Medical Papers with Azure and Text Analytics for Health

Since the beginning of COVID pandemic more than a year ago, there have been more than 400000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus - and therefore some help from AI is highly needed. In this post, we will show how we can extract some knowledge from scientific papers, gain insights, and build a tool to help researcher navigate the paper collection in a meaningful way. Read More ›


Виртуальный нано-хакатон #openbirthday 2021: Театр роботов

Приглашаю в очередной раз нетрадиционным способом отметить мой день рождения. 27 марта в 20:00 добро пожаловать в онлайн! Read More ›

azure – Conversational AI on Microsoft Platform

Hello, bot!

During the pandemic, we all found ourselves in isolation, and relying more and more on effective electronic means of communication. The amount of digital conversations increased dramatically, and we need to rely on bots to help us handle some of those conversations. In this blog post, I give brief overview of conversational AI on Microsoft platform and show you how to build a simple educational bot to help students learn. Read More ›

Azure – Azure

Learn Applied Data Science and Get Certified with Microsoft and Udacity

Unlike theoretical data science, applied data science involves additional steps to manage the lifetime of a model, which are commonly called MLOps. Azure Machine Learning is a service that conveniently supports MLOps practices, so getting to know it seems like a good idea. In this post, I will talk about the best way to learn (Udacity), and to demonstrate your knowledge (Certification). Read More ›

azure – Azure

Dual DSVM Setup for Cost-Effective Experimentation

It is estimated that a Data Scientist spends about 80% of her time on data preparation, and not on the model training. If your training setup is just a GPU virtual machine in the cloud - it means that you are spending 80% of its uptime in vain, because GPU is not utilized. For more cost-effective way we may want to split the work between two virtual machines, one for data preparation, and another one for actual training. This is exactly the setup I used for some time when working in Microsoft CSE, before switching to AzureML, so I will share my knowledge here. Read More ›