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Description
Topic: Connecting your unstructured data with LLMs
We are meeting for another happy hour/discussion group about Unstructured Data and its future in machine learning and LLM apps!
What we’ll do:
Have some snacks and refreshments. Have a couple of talks and then unstructured networking.
***
Schedule
6:00-6:55 - Open Doors, food and open networking
7:00-7:30 - TBD - Stephen Batifol, Zilliz
7:30-8:00 - Large Language Models ❤️ Knowledge Graphs - Michael Hunger, Neo4j
8:00-8:05 - Small Break
8:05-8:30 - TBD - Jakob Pörschmann, Google Cloud
8:30-10:00 - Networking
Who Should attend:
Anyone interested in talking and learning about Unstructured Data and LLM Apps.
When:
Oct 2nd, 2024
6:00PM
Google Cloud
Sponsored by Zilliz & Google Cloud
***
Tech Talk 1: TBD
Speaker: Stephen Batifol, Developer Advocate, Zilliz
Abstract: TDB
Tech Talk 2: Large Language Models ❤️ Knowledge Graphs
Speaker: Michael Hunger, Head of Product Innovation, Neo4j
Abstract: As you might have experienced, LLMs are powerful but not always trustworthy assistants. With a combination of a knowledge graph and vector search, you can provide the LLM with the correct, relevant context information it needs to answer your user's questions. GraphRAG is an advanced RAG (retrieval augmented generation) that allows for higher quality and explainable responses. In this talk, we'll explain and demonstrate the building blocks of such an approach combining vector search in Milvus with Graph search in Neo4j and show an example of code and live in action. Of course, nothing is perfect; it's important to walk through the challenges of building such GenAI apps and how to address them.
Tech Talk 3: TBD
Speaker: Jakob Pörschmann, AI/ML Customer Engineer, Google Cloud
Abstract: TBD
Check out Zilliz blog, join our Discord and Milvus Github
***
📸Important note: Please be advised that this event will be recorded and photographed. If you prefer not to be included in any recordings or photographs, please do not hesitate to let us know during the event. Your comfort and privacy are important to us.
We are meeting for another happy hour/discussion group about Unstructured Data and its future in machine learning and LLM apps!
What we’ll do:
Have some snacks and refreshments. Have a couple of talks and then unstructured networking.
***
Schedule
6:00-6:55 - Open Doors, food and open networking
7:00-7:30 - TBD - Stephen Batifol, Zilliz
7:30-8:00 - Large Language Models ❤️ Knowledge Graphs - Michael Hunger, Neo4j
8:00-8:05 - Small Break
8:05-8:30 - TBD - Jakob Pörschmann, Google Cloud
8:30-10:00 - Networking
Who Should attend:
Anyone interested in talking and learning about Unstructured Data and LLM Apps.
When:
Oct 2nd, 2024
6:00PM
Google Cloud
Sponsored by Zilliz & Google Cloud
***
Tech Talk 1: TBD
Speaker: Stephen Batifol, Developer Advocate, Zilliz
Abstract: TDB
Tech Talk 2: Large Language Models ❤️ Knowledge Graphs
Speaker: Michael Hunger, Head of Product Innovation, Neo4j
Abstract: As you might have experienced, LLMs are powerful but not always trustworthy assistants. With a combination of a knowledge graph and vector search, you can provide the LLM with the correct, relevant context information it needs to answer your user's questions. GraphRAG is an advanced RAG (retrieval augmented generation) that allows for higher quality and explainable responses. In this talk, we'll explain and demonstrate the building blocks of such an approach combining vector search in Milvus with Graph search in Neo4j and show an example of code and live in action. Of course, nothing is perfect; it's important to walk through the challenges of building such GenAI apps and how to address them.
Tech Talk 3: TBD
Speaker: Jakob Pörschmann, AI/ML Customer Engineer, Google Cloud
Abstract: TBD
Check out Zilliz blog, join our Discord and Milvus Github
***
📸Important note: Please be advised that this event will be recorded and photographed. If you prefer not to be included in any recordings or photographs, please do not hesitate to let us know during the event. Your comfort and privacy are important to us.
Ajouté par
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