17 Python Libraries Every AI Engineer Should Know
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⏱️ Timestamps
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.
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