دانلود کتاب هوش مصنوعی عامل‌دار برای مهندسان DevOps: ساخت گردش‌های کاری CICD، زیرساخت و عملیات مستقل

DevOps is heavy work. Engineers write pipelines, review code and configurations, interpret logs and incidents, automate infrastructure, and understand cloud and server environments. The work is not only about execution; it is also about interpretation. Engineers constantly translate intent into pipelines, configurations, and infrastructure. For this reason, having an assistant can be beneficial. At the same time, DevOps relies on predictability and repeatability. Ambiguous tools introduce operational risk. If generative AI is going to be used in DevOps, it must be clearly idx_0bbadeb0understood, because poorly understood tools introduce risk rather than reduce it. What is generative AI? Generative AI refers to software idx_98378c8fsystems that generate new content rather than executing predefined instructions. These systems can produce text, code, configurations, and summaries. This is especially useful in a DevOps context because much of DevOps work is text-based. The output from generative AI is not hard-coded. It is based on learned patterns rather than fixed rules. This is useful in dynamic systems that are constantly evolving, but it also means that generative AI should not be treated in the same way as deterministic automation. One of the biggest misconceptions about generative AI is that it is just another form of automation. Automation executes known steps, whereas generative AI can propose new outputs and is more malleable and dynamic. This distinction matters because accountability in DevOps never shifts to the tool. Humans remain responsible for approving, validating, and deploying changes. What is an LLM? Whenever generative AI is discussed, large language models (LLMs) is usually part of the conversation. An LLM is a type generative AI model trained on large volumes of text, code, documentation, and other structured language-like data. Most modern LLMs are based on transformer architectures, which enable models to analyze a prompt and generate a likely continuation. A token may be a word, part of a word, a punctuation mark, a line of code, or another small unit of text that the model can process.