Machine Learning

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!

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Machine Learning Illustration

Machine Learning fields we work in

We can boast of 3+ years of hands-on experience in Machine Learning fields: NLP (Natural Language Processing) and NLU (Natural Language Understanding).

Tasks that we solve in our Machine Learning cases:

About open-source models that we utilize

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).

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 Slack bot development based on Machine Learning algorithms and the circle of our interests in ML is constantly growing.

Our development process

Clarify and set project requirements
Create a system design and UX/UI wireframes
Define and prioritize development tasks, create a backlog, prioritize all the tasks and provide estimates
Select the tasks from the top of the backlog for a 2-week sprint cycle
Deliver sprint results and iterate based on the feedback
Finally, deploy the project after all goals were met

Our projects

USA

Dashboard for the Employee Engagement Platform

Dashboard for the Employee Engagement Platform
UK

Charity Donation Platform

Charity Donation Platform
USA

Logistics Dashboard for the Digital Fulfillment Platform

Logistics Dashboard for the Digital Fulfillment Platform
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Other services we provide

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