17 Python Libraries Every AI Engineer Should Know

Video Description

Want to start freelancing? Let me help: https://academy.datalumina.com/freelance Want to learn real AI Engineering? Go here: https://academy.datalumina.com/accelerator 💼 Need help with a project? Work with me: https://www.datalumina.com/ ⏱️ Timestamps 00:00 Introduction 00:50 Pydantic 01:25 Pydantic Settings 02:17 Python Dotenv 02:39 FastAPI 03:43 Celery 05:21 Databases 06:21 SQLAlchemy 06:46 Alembic 07:25 Pandas 08:13 LLM Model Providers 09:11 Instructor 10:45 LLM Frameworks 12:56 Vector Databases 14:16 Observability 15:37 DSPy 17:02 PDF Parsers 18:05 Jinja 📌 Description In this video, I cover the evolving role of AI engineers and the necessity of mastering key Python libraries for success in the field. I highlight 17 crucial libraries that we utilize within our projects, addressing the shift in responsibilities for AI engineers from model creation to integration of pre-trained models. Key libraries such as Pydantic for data validation, FastAPI for API development, and Celery for task management are discussed, alongside important database tools like PostgreSQL and MongoDB. This video also introduces frameworks like Langchain and Llama Index, emphasizing the need for familiarity with their complexities. Additionally, it covers vector databases and specialized AI tasks, concluding with a project repository aimed at enhancing the implementation of generative AI applications. 👋🏻 About Me Hi there! I’m Dave Ebbelaar, founder of Datalumina®, and I’m passionate about helping data professionals and developers like you succeed in the world of data science and AI. If you enjoy the tutorial, make sure to check out the links in this description for more resources to help you grow. At Datalumina, we help individuals and businesses unlock the full potential of AI and data by turning complexity into capability. Whether you're learning Python, freelancing, or building cutting-edge AI apps, we provide the tools, guidance, and expertise to help you succeed.