From the course: Fundamentals of AI Engineering: Principles and Practical Applications
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Next steps and resources
From the course: Fundamentals of AI Engineering: Principles and Practical Applications
Next steps and resources
- [Instructor] The AI engineering landscape is evolving at a remarkable pace. What's cutting edge today, might be standard practice tomorrow. This rapid evolution makes it both exciting and challenging to stay current, but don't worry, I'll share some resources and strategies to help you continue your learning journey. First, let's talk about community resources. The AI engineering community is incredibly collaborative and open. Platforms like Hugging Face have become central hubs where researchers and practitioners share models, data sets, and code. Their forums and discussion spaces are invaluable for solving specific problems and learning best practices. GitHub remains an essential resource with repositories like LangChain, LlamaIndex, ChromaDB, and Sentence Transformers, providing both code and comprehensive documentation. Following these projects not only gives you access to powerful tools, but also helps you understand emerging patterns and techniques. For deeper theoretical…
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