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OPTIMIZING RAG RETRIEVAL

July 31

FINE-TUNE LLMS

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7/31 10 AM PT: Optimizing RAG retrieval8/7 10 AM PT: Fine-tuning LLMs for
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UPCOMING DEMOS WITH Q&A:

OPTIMIZING RAG RETRIEVAL

July 31

FINE-TUNE LLMS

August 7

FINE-TUNING EMBEDDING MODELS

August 14
DEMO


WEEKLY DEMO
WITH Q&A

See a demo of Snorkel Flow for generative AI use cases such as RAG optimization
and LLM fine-tuning, and chat with one of our machine learning solution
engineers.

Each week, we'll show you (step by step) how Snorkel Flow is used to support a
different AI use case, whether it's classifying chatbot utterances or
fine-tuning a RAG embedding model.

We'll be happy to answer any questions about the demo, Snorkel Flow and what
we're seeing in enterprise AI.


SIGN UP FOR ONE OR FOR ALL . . .

Select the one(s) you want on the form

JULY 31, 10-10:30 AM PT


OPTIMIZING RAG RETRIEVAL

We'll create labeled question/context/ answer triplets and document metadata,
and use them to fine-tune the embedding model and chunk retrieval.

AUGUST 7, 10-10:30 AM PT


FINE-TUNING LLMS FOR ENTERPRISE ALIGNMENT

We’ll create functions to label a set of compliant and non-compliant question/
answer pairs as approved or rejected, and use them to fine-tune an LLM.

AUGUST 14, 10-10:30 AM PT


FINE-TUNING EMBEDDING MODELS

We’ll create functions to extract information, structure and metadata from
documents, and use them to fine-tune an embedding model for RAG.

AUGUST   10-10:30 AM PT


FINE-TUNING EMBEDDING MODELS

We’ll create functions to extract information, structure and metadata from
documents, and use them to fine tune an embedding model for RAG.


PRODUCT

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SOLUTIONS


AI APPLICATIONS

 * Document classification
 * Named entity recognition
 * Information extraction
 * Conversational AI
 * Sentiment analysis


INDUSTRY USE CASES

 * AI for banking
 * AI for government
 * AI for healthcare
 * AI for insurance
 * AI for telecom


TECHNOLOGY

 * Snorkel AI Technology
 * Data-centric AI
 * Data labeling
 * Foundation models
 * Generative AI
 * Large language models
 * Programmatic labeling
 * Weak supervision
 * Snorkel research project


CASE STUDIES

 * Apple
 * Big four consulting firm
 * Fortune 50 bank
 * Fortune 500 telecom
 * Global custodian bank
 * Global financial services leader
 * Global insurance leader
 * Google
 * Intel
 * Memorial Sloan Kettering
 * Stanford Medicine
 * Tide
 * Top 5 biotech
 * Top US bank


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