دانلود کتاب آموزش عملی APIها برای هوش مصنوعی و توسعه داده پایتون با FastAPI

To succeed in AI, first master APIs. Becoming skilled at APIs is more valuable than ever, thanks largely to the growth of artificial intelligence, machine learning, and data science. But learning a such wide-ranging skill is intimidating. How is it to be done? You can take comfort in the fact that you don’t have to learn every skill, and certainly not all at once. Pick up one skill at a time through hands-on practice. Each skill you learn makes the next one easier, like building blocks. If you’re reading this book, you want to build your skills. I have found that the best way to do that is through hands-on coding. If you do your coding in the open by publishing your code in a public repository and blogging and sharing what you create, you can pass along your knowledge to help others. You’ll also build a solid portfolio of work that provides a concrete demonstration of your expertise to employers. Data scientists use APIs all the time, so there’s a temptation to think there’s nothing new to learn about using APIs. Isn’t calling an API just a few lines of code? It’s true that making one call to a REST API is a simple task, which is certainly a reason they have become so prevalent. But using an API in a way that is robust and resilient—and that doesn’t cause problems for the provider—requires more care. This book will teach some techniques you may not have learned yet, such as: • Developing and deploying APIs • Creating software development kits (SDKs) and API clients • Creating and publishing Python packages to PyPI • Publishing machine learning models as APIs • Creating Streamlit data apps • Creating Airflow data pipelines • Creating generative AI applications using LangChain and ChatGPT