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The choice between a self-hosted LLM and a public API is one of the most critical architectural decisions teams face when building AI products. Both options provide access to language models that can power chatbots, analysis tools, automation systems, and internal assistants. But in production environments, the difference goes far beyond the possibility of generating answers. The choice affects cost structure, latency, data control, security, maintenance complexity, team skil

Building a high-quality chatbot is not just about generating good-looking responses. In production environments, quality is a multidimensional concept that covers a wide range of metrics and requires strict alignment with business expectations. Modern LLM-powered chatbots operate in a dynamic, high-traffic space where even small degradations in output quality can quickly lead to user frustration, increased support load, or financial and reputational risks. Many teams still tr

Starting a new career in any domain is always challenging and exciting. In the field of data science, it is even more stressful and...

Despite the rapid development of Computer Vision and Deep Learning tools, convolutional neural networks still perform great in solving...

One of the biggest challenges for new comers into the world of deep learning is a framework selection. And it is really confusing to...