Machine Learning (ML) is a data analysis technology that allows systems to learn how to solve different problems. It is based on the idea that analytical systems can learn to recognize patterns and make decisions without human involvement.
Machine Learning is becoming one of the most popular technologies in the world bringing its capabilities to people’s work and lives. The ideas and prospects of Machine Learning we realize in Natural Language fields. We are going to show here how do we work with Machine Learning algorithms, which techniques we utilize, what kind of tasks we solve and tell you about our project. Let’s go!
We mainly work in Machine Learning fields: NLP (Natural Language Processing) and NLU (Natural Language Understanding). In spite of having experience with both of them, we try to concentrate on the NLU field.
We have a big experience in natural language understanding because we work in this field for about 3 years. The number of tasks we solve in this field grows constantly.
Nowadays there a lot of powerful open-source Machine Learning libraries and models that are freely available on the market. We don’t hesitate to use them to improve our Machine Learning algorithms and NLU potential. Here are some of them:
Here you can check the more detailed article in which we described how to build a semantic search pipeline using open-source components and a little bit of coding (based on our project OneBar).
In addition to consulting services, we are all working on Onebar. OneBar is a search tool (based on Machine Learning) for teams that helps them boost productivity and promote knowledge sharing. It integrates with Slack and provides a seamless search experience through a convenient interface. Here we try to use and experiment with the most progressive features of Machine Learning in the field of NLP. Read more about OneBar via this link https://onebar.io/.
Machine Learning has a really borderless potential for business, science, human lives, and humanity in general. For instance, we develop Chatbots based on Machine Learning algorithms and the circle of our interests in ML is constantly growing.
Our clients are businesses of different types and sizes. We developed projects for companies in a range of industries: finance, media, healthcare, education, government, real estate/property, social media, travel/transport, e-business, e-commerce, to name a few.
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Kenny R. Lienhard
CTO, Medignition Inc
We wanted to implement a new education platform that offered online courses in finance. The scope focused on the implementation of multiple user interfaces. UpsilonIT used React, GraphQL, and GatsbyJS. UpsilonIT had strong coding skills. The app had a clean codebase and complied with all specifications. They complied with our requirements for the UI. We communicated daily via Slack.
CTO, Civic Connect
Upsilon’s work was always on time and met all customer expectations. Their team had an ease of communication but what was the most impressive about them is the integrity and work ethic of each member. We had a business manager, a lead architect, and a mix of senior and regular engineers. The workflow was extremely effective - the teams had a regular cadence for communication.
Creative Consultant, Etage Group
We needed help with mobile app development, data analytics, and beacon installation to be able to collect, store, and analyze our customer data. The finished application met the requirements perfectly, with the team finding an excellent solution to collect, analyze, and store customer data. They were highly professional throughout the work, always making themselves available, and responding well to any changes.