دانلود کتاب ساخت خدمات هوش مصنوعی مولد با FastAPI

Generative AI (GenAI) is taking the world by storm since the release of technologies like ChatGPT. This new type of AI can create content in various modalities (such as text, audio, video, etc.) by learning to mimic patterns from its training data. With the increased advancement in GenAI capabilities, many businesses are investing in off-the-shelf or custom AI tools. These tools require maintainable and scalable backend services that can adapt to high demand. AI capabilities are exciting because they open the door to endless possibilities that unlock the potential for new tools. Before generative AI, developers had to write scripts and train optimization models to build automation and data pipelines for their processing of unstructured data like corpora of texts. This process could be tedious, error-prone, and applicable only to limited use cases. However, with the rise of GenAI models such as large language models (LLMs), we can now digest, compare, and summarize unstructured datasets and documents; reword complex ideas; and generate visualizations and illustrations. While most generative models such as ChatGPT are excellent at what they do on their own, can you imagine the possibilities when we connect them to the internet, our own databases, and other services? If we can just “talk” to our services in natural language or give them some image, video, or audio and get them to do things for us, it opens up so many opportunities to create newly accessible and automated applications. Chatbots are not the only apps that we can create with such generative models. There is so much more we can do. We can create backend service agents that can perform various complex tasks requiring comprehension, logical reasoning, and analysis of texts. By connecting our generative models to existing services and the internet, we are giving our AI services additional data to enrich their understanding of the problem at hand. For instance, a company can use an open source, in-house, finetuned LLM to parse purchase orders, generate invoices, and validate data against their customer database before placing an order with a payment system. This is where generative models shine. Other use cases can include content management systems that can help users with generating content and website builders that can suggest imagery, icons, and user interface (UI) components to fast-track the site’s design. There is a catch. LLMs and other generative models require heavy processing power and memory to function, and it is not clear what deployment patterns and integration layers the developers should use to leverage these models. Building generative AI services is challenging because you need to balance scalability, security, performance, and data privacy. You’ll also want the ability to moderate, retrain, and optimize these services for real-time inference. These challenges will be different for every organization, and how you build your generative AI services will depend on your existing software systems and services. Existing resources and documentation provide the necessary information to get started with training custom models and fine-tuning large language models. However, most developers may continue to face challenges in packaging and deploying these novel generative models as part of existing software systems and services. My aim with this book is to show you how to productionize GenAI by understanding the end-to-end process in building and deploying your own AI services with tools such as the FastAPI web framework.