Long-form writing from the people who ship the systems — not marketing. New essays go out roughly weekly.
Where artificial intelligence actually improves business processes in 2026 - document intake, support, voice agents, sales research, forecasting, inspection, fraud - where AI agents help and where they break legacy systems, and how to scope a first AI process optimization project.
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A buyer's guide to data science consulting for founders and CTOs: what consultants deliver, what it costs in 2026 (hourly, project, pilot, dedicated team), in-house vs outsourcing vs consulting partner, nine questions to ask before you sign, and the red flags of a failed engagement.
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A practitioner's guide to machine learning pipelines: the seven stages from ingestion to retraining, an architecture diagram with training and serving paths, how to choose orchestration tools, a real example with code, what changes for deep learning, and what breaks in production.
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Every day we face an enormous volume of text and voice data. How can we systemize this information and decide on the right response? Here is where NLP comes in - and here is how ecommerce uses it for search, recommendations, support, and sentiment analysis.
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How computer vision is used in healthcare today - disease diagnosis from medical images, surgical assistance, remote patient monitoring, drug discovery - plus the challenges that stop most projects (data privacy, regulation, domain shift) and the market outlook.
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What to do when there is not enough data to train a machine learning model: how much data you actually need, open data sources, manual labeling, simulation, synthetic data generation, and what pretrained models changed - from a team that ships models on small, messy datasets.
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> The presented article describes a tool that is part of the healthcare platform we have developed.
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Generative Adversarial Networks (GANs) that appeared in 2014 made it possible not only to perform image style transfer but also to generate text to speech with any voice.
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In this article, we discuss topics such as adapting transformer architecture from NLP for image processing. We will explore novel vision transformers architectures and their application to сomputer vision problems: object detection, semantic segmentation, depth prediction.
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