AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment
What if you could give your LLM the right context without retraining it? This framework adapts MLOps for modern generative AI applications.
#1about 2 minutes
The convergence of ML and DevOps in MLOps
MLOps combines machine learning management with DevOps practices to create an integrated system where developers and data scientists work in synergy.
#2about 4 minutes
Understanding retrieval-augmented generation systems
RAG systems enhance large language models by retrieving relevant information from a custom knowledge base and augmenting the user's prompt with that context.
#3about 4 minutes
Introducing the MLOps life circle framework
The MLOps life circle provides a four-quadrant template for managing the entire machine learning lifecycle, covering data management, model development, validation, and deployment.
#4about 5 minutes
Adapting the MLOps framework for RAG systems
The MLOps life circle is adapted for RAG by replacing model development with model selection and augmentation, focusing on tools like vector databases and context tuning.
#5about 3 minutes
A deep dive into context tuning for RAG
Context tuning improves RAG responses by augmenting user queries with relevant information retrieved from multiple sources like order details, FAQs, and past interactions.
#6about 1 minute
Using the framework to optimize your toolchain
The life circle framework helps visualize your entire RAG system, allowing you to identify necessary components and select a minimal set of tools to reduce context switching.
#7about 5 minutes
Q&A on agents, vectorization, and chunking
The speaker answers audience questions about integrating agents, the process of vectorizing data, elaborating on context tuning, and handling document chunking.
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