Artificial Intelligence

Today it is hard to imagine a business that doesn’t use machine learning in analytics and decision-making. However, the development and deployment of ML models can be very complicated and resource-consuming. Even experienced data scientists and ML engineers could benefit from effective automation. AutoML is a way to automate machine learning in your enterprise. In this article, we will talk about the benefits of AutoML, its operating principles, and ways to use it in your business.

The rapid advancement of AI has transformed the way developers approach software creation, problem-solving, and collaboration. AI tools are no longer just experimental—they’ve become essential components of modern development environments. This article will be especially useful for developers who want to automate repetitive tasks, enhance code accuracy, and improve project management. We offer an overview of some of the best AI tools currently available, including pricing and key features, so you can choose the right tools to fit your needs and projects.

In the last few years, artificial intelligence has become an omnipresent technology. Many businesses, including world leading companies, apply AI for various purposes: business intelligence, performance analytics, enhancing user experience, optimization, and more. In this article, we will look at 9 companies that use artificial intelligence. Hopefully, they can inspire you and give some suggestions about how you can use AI creatively in your business.

In this article, we’ll explore how Bidirectional Encoder Representations from Transformers (BERT) works, and why it’s called a universal tool for solving dozens of natural language processing tasks.

This is the second article in our series “LLMs for Business.” In it, we explore how to fine-tune large language models and use them safely so they don’t harm your business.

We've seen many articles on large language models (LLMs) aimed at the general public and decision-makers. Many of them are either overly simplistic or, conversely, cover too complicated technical aspects. In our blog post, we take a balanced approach to explaining the essence of LLMs and their fundamental processes. In the first article of a series dedicated to large language models, we discuss their foundational principles and various approaches to running them.