Anton Ovchinnikov

For many years, chatbots have been quietly moving from the screens of sci-fi movies to commercial applications. Companies like Google, Amazon, Apple, IBM, and Microsoft, along with an ever-increasing array of AI startups, have been gradually perfecting Conversational User Interfaces (CUIs) that a...

In the previous post we discussed which models we tried for sentiment classification and which one has demonstrated the best performance. In this post, we’ll show you how to visualize our under-the-hood findings so that others can see the results of our analysis. You can see our twitter senti...

In previous posts we have discussed the steps needed to understand and prepare the data for Social Movie Reviews. Finally, it is time to run the models and learn how to extract meanings hidden in the data. This blog post deals with the modeling step in the Data Scientist’s Kitchen. At the...

In the previous post we discussed how we created an appropriate data dictionary. In this post we’ll address the process of building the training data sets and preparing the data for analysis. The training process aims to reveal hidden dependencies and patterns in the data that will be analyzed...

In the previous post we discussed the structure of the tweet data. In this post we’ll address the process of selecting or building the right data dictionary for our purpose. What constitutes a good dictionary? A crucial data set for any kind of text mining is a dictionary. As for sentiment...

In the previous post we outlined the basic scientific method used and formalized the problem statement we are solving, which is, “Based on of the tweets of English-speaking population of the United States related to selected new movie releases, can we identify patterns in the public’s sentiments t...

There is a broad and fast-growing interest in data science and machine learning. It is fueled by an explosion in business applications that rely on automated detection of patterns and behaviors hidden in the data, that can be found by software and exploited to dramatically improve the way we mark...

In the previous four blog posts in this series we covered the reference architecture of a general purpose In-Stream Processing Service blueprint. To recap, here is a list of shortcuts to the blogs in that series: … Chatbots in retail: Chatbot technology and architecture advanceRead More...

As we explained in our introduction to this series of posts, we are exploring a data scientist’s methods of extracting hidden patterns and meanings from big data in order to make better applications, services, and business decisions. We will perform a simple sentiment analysis of a real publ...

This article introduces the Grid Dynamics blueprint for in-stream processing. It is based on our experience and the lessons we have learned from multiple large-scale client implementations. We have included cloud-ready configuration examples for Apache Kafka, Spark...

This post contains a brief survey of better-known products related to in-stream processing that are available on the market at the time of this writing. In this survey, we focus specifically on critical architectural differentiations, rather than functional differences, that affect why custome...

Now that we have introduced the high-level concepts behind In-Stream Processing and how it fits into the Big Data and Fast Data landscapes, it is time to dive deeper and explain how In-Stream Processing works. As we already know, In-Stream Processing is a service that takes events as input and...

In-stream processing is a powerful technology that can scan huge volumes of data coming from sensors, credit card swipes, clickstreams and other inputs, and find actionable insights nearly instantaneously. For example, in-stream processing can detect a single fraudulent transaction in a stream...