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4
[] A production issue
5
[] CSV file handling risks
6
[] Embedding models not suitable
7
[] Inference layer experiments and use cases
8
[] AWS service handling the issue
9
[] Salad testing and insights
10
[] OpenAI vs Customization
11
[] Difference between Olama and VLLM
12
[] Fine-tuning of small LLMs
13
[] Evaluation framework
14
[] MLOps for efficient ML
15
[] Determining the pricing of tools
16
[] Manage Dependency Risk
17
[] Get in touch with Syed on LinkedIn
18
[] ML Engineers are now all AI Engineers
19
[] The hard framework
20
[] Wrap up
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Dive into a comprehensive podcast episode exploring Retrieval Augmented Generation (RAG) with Syed Asad, Lead AI/ML Engineer at KiwiTech. Gain insights on semantic vector searches, vector databases, and cutting-edge RAG techniques reshaping AI capabilities. Learn about production issues, CSV file handling risks, embedding model challenges, and inference layer experiments. Explore AWS services, OpenAI customization, differences between Olama and VLLM, and fine-tuning small language models. Discover evaluation frameworks, MLOps for efficient machine learning, tool pricing strategies, and dependency risk management. Understand the evolving role of ML engineers in the AI landscape and explore the hard framework for AI development.
Retrieval Augmented Generation - Techniques and Applications