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Data Engineering|Published: Jan 22, 2026

Unsupervised learning for computer vision

Traditional machine learning creates a significant bottleneck: manual labeling can cost millions of dollars for large-scale projects, require months of expert time, and remain prone to human error and inconsistency. Unsupervised learning offers a fundamentally different approach, allowing algorithms to discover structure and patterns in visual data without any human-provided labels. In this article, we will discuss unsupervised approaches used for training image models, with no labels given a priority.

Data Engineering|Published: Oct 7, 2025

What is data orchestration?

Data orchestration can be defined either as an automation or as a data management process. In this article, we will look closely at both of these definitions, how they are different from each other. We will also talk about some of the most popular tools used by engineers worldwide.

Data Engineering|Published: May 13, 2025

Data lake for edge reference architecture: What is it, and how does it integrate with modern edge computing solutions?

Read on to find out what they are and what makes them a valid storage option for solutions built around edge reference architecture. You will also learn how the combination of data lakes and edge computing applies in real life.

Data Engineering|Published: Apr 9, 2025

VirtusLab at Data Innovation Summit 2025: Let’s talk data, AI, and tea?

We’re excited to announce that VirtusLab will be attending the Data Innovation Summit 2025 as a Venue Partner! Whether you're deep into machine learning, curious about data maturity, or just looking for some good conversation (and maybe a cup of tea), we’d love to meet you there.

Data Engineering|Published: Apr 7, 2025

Are LLMs stealing the spotlight from classic Machine Learning?

The LLMs as we know them didn’t appear out of thin air. AI has roots dating back over 100 years, when early concepts like fuzzy logic emerged. Then came mathematical models that contributed to the development of neural networks. But will LLMs replace classic Machine Learning for good?

Data Engineering|Published: Mar 4, 2025

What are the benefits and advantages of machine learning?

Machine learning enhances the accuracy and precision of various tasks by processing large amounts of data and identifying patterns. If your organization is considering implementing a machine learning-based solution, we encourage you to explore the advantages and benefits of ML and discuss some potential limitations and what you can do to minimize them.

Data Engineering|Published: Jan 21, 2025

What is a data platform?

Discover how a robust data platform streamlines the data lifecycle, boosts analytics, ensures security, and drives business insights. We elaborate on what a data platform is, its layers, benefits, and what there is to know when you build one.

Data Engineering|Published: Jan 15, 2025

What is data transformation?

Data transformation involves altering, refining, and structuring information into a standardized and usable format. It plays a key role in enabling effective data analysis, supporting decision-making, and driving organizational growth. Read more about different types of data transformation, as well as its benefits and challenges.

Data Engineering|Published: Jan 10, 2025

How to manage data for your project? Comparing batch processing with stream processing.

In this article, we will compare two approaches to data processing, namely batch and stream processing. Each of them has different characteristics and serves different purposes, but we will attempt to break them down to help you decide which one would better suit your project.

Data Engineering|Published: Nov 24, 2023

What does data transformation enable data analysts to accomplish?

Read everything about data transformation encompassing SQL, GUI, Scala, and Python, to get clean and structured data for your decision-making process. We delve into the technology and which is best for what project.

Data Engineering|Published: Sep 27, 2023

What is data mesh? Redefining data platform architecture

Data Mesh has been founded on four key principles that revolutionized data management. It focuses on decentralization, domain-oriented teams, data-as-a-product, self-serve platform, and federated governance.