talk-data.com
People (1 result)
Activities & events
| Title & Speakers | Event |
|---|---|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #1: Recent Advancements in Multi-Modal Gen AI - RAG, Embeddings, Foundation Models (Segment Anything 2) by Antje Barth (Principal Engineer, Gen AI) Talk #2: Fine-Tuning Multi-Modal Models with PEFT, Textual Inversion, and ControlNet by Shelbee Eigenbrode (Principal SA Lead, Gen AI) Talk #3: Multi-Modal RAG with True Multi-Modal Embeddings (not just text embeddings!!) by Chris Fregly (Principal Engineer, Gen AI) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG - Segment Anything Model v2, ImageBind, Embeddings, Fine-Tuning
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #1: Recent Advancements in Multi-Modal Gen AI - RAG, Embeddings, Foundation Models (Segment Anything 2) by Antje Barth (Principal Engineer, Gen AI) Talk #2: Fine-Tuning Multi-Modal Models with PEFT, Textual Inversion, and ControlNet by Shelbee Eigenbrode (Principal SA Lead, Gen AI) Talk #3: Multi-Modal RAG with True Multi-Modal Embeddings (not just text embeddings!!) by Chris Fregly (Principal Engineer, Gen AI) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG - Segment Anything Model v2, ImageBind, Embeddings, Fine-Tuning
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #1: Recent Advancements in Multi-Modal Gen AI - RAG, Embeddings, Foundation Models (Segment Anything 2) by Antje Barth (Principal Engineer, Gen AI) Talk #2: Fine-Tuning Multi-Modal Models with PEFT, Textual Inversion, and ControlNet by Shelbee Eigenbrode (Principal SA Lead, Gen AI) Talk #3: Multi-Modal RAG with True Multi-Modal Embeddings (not just text embeddings!!) by Chris Fregly (Principal Engineer, Gen AI) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG - Segment Anything Model v2, ImageBind, Embeddings, Fine-Tuning
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #0: Introduction by Chris Fregly (Principal SA, Generative AI) and Antje Barth (Principal Developer Advocate, Generative AI @ AWS) Talk #1: Multimodal RAG with Milvus + Ray Data + UForm tiny encoders by Christy Bergman @ Zilliz We've seen an influx of powerful multimodal capabilities in many LLMs. In this talk, I'll vectorize a dataset of images and texts into the same embedding space, store them in Milvus, retrieve all relevant data using multilingual texts and/or images and input multimodal data as context into GPT-4o. Speaker Bio: Christy Bergman is a passionate Developer Advocate at Zilliz. She enjoys creating demos, tutorials, speaking, organizing events, and helping people learn about Unstructured Data and AI. Before Zilliz, Christy worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Talk #2: Prompt, Model, and Multi-Agent Optimization with DSPy by Alejandro Herrera (Solution Architect @ Snowflake) Talk #3: Model Optimization, Pruning, and Distillation for Faster Model Inference by Shelbee Eigenbrode (Principal SA, Gen AI @ AWS, co-author of Generative AI on AWS by O'Reilly) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG w/ Milvus + Multi-Agent Optimization w/ DSPy + LLM Distillation
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #0: Introduction by Chris Fregly (Principal SA, Generative AI) and Antje Barth (Principal Developer Advocate, Generative AI @ AWS) Talk #1: Multimodal RAG with Milvus + Ray Data + UForm tiny encoders by Christy Bergman @ Zilliz We've seen an influx of powerful multimodal capabilities in many LLMs. In this talk, I'll vectorize a dataset of images and texts into the same embedding space, store them in Milvus, retrieve all relevant data using multilingual texts and/or images and input multimodal data as context into GPT-4o. Speaker Bio: Christy Bergman is a passionate Developer Advocate at Zilliz. She enjoys creating demos, tutorials, speaking, organizing events, and helping people learn about Unstructured Data and AI. Before Zilliz, Christy worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Talk #2: Prompt, Model, and Multi-Agent Optimization with DSPy by Alejandro Herrera (Solution Architect @ Snowflake) Talk #3: Model Optimization, Pruning, and Distillation for Faster Model Inference by Shelbee Eigenbrode (Principal SA, Gen AI @ AWS, co-author of Generative AI on AWS by O'Reilly) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG w/ Milvus + Multi-Agent Optimization w/ DSPy + LLM Distillation
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #0: Introduction by Chris Fregly (Principal SA, Generative AI) and Antje Barth (Principal Developer Advocate, Generative AI @ AWS) Talk #1: Multimodal RAG with Milvus + Ray Data + UForm tiny encoders by Christy Bergman @ Zilliz We've seen an influx of powerful multimodal capabilities in many LLMs. In this talk, I'll vectorize a dataset of images and texts into the same embedding space, store them in Milvus, retrieve all relevant data using multilingual texts and/or images and input multimodal data as context into GPT-4o. Speaker Bio: Christy Bergman is a passionate Developer Advocate at Zilliz. She enjoys creating demos, tutorials, speaking, organizing events, and helping people learn about Unstructured Data and AI. Before Zilliz, Christy worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Talk #2: Prompt, Model, and Multi-Agent Optimization with DSPy by Alejandro Herrera (Solution Architect @ Snowflake) Talk #3: Model Optimization, Pruning, and Distillation for Faster Model Inference by Shelbee Eigenbrode (Principal SA, Gen AI @ AWS, co-author of Generative AI on AWS by O'Reilly) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Multi-Modal RAG w/ Milvus + Multi-Agent Optimization w/ DSPy + LLM Distillation
|
|
++ Due to daylight savings time (US), we start at 4pm GMT/London ++ RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #0: Introduction by Chris Fregly (Principal SA, Generative AI) and Antje Barth (Principal Developer Advocate, Generative AI) Talk #1: Advanced Retrieval Augmented Generation (RAG) with Cohere on AWS by Jay Alammar, Director @ Cohere The rise of large language models is inspiring a wide variety of applications. Retrieval-Augmented Generation (RAG) stands as one of the leading use cases for developers and enterprises alike who need to empower the conversational ability of an LLM with grounding on specific data. This talk takes you beyond basic search-then-generate RAG to an advanced setup that utilizes reranking, query re-writing, and citations that vastly improve RAG systems. Talk #2: Parameter-Efficient Fine-Tuning (PEFT) by Shelbee Eigenbrode (Principal SA, Generative AI) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Advanced RAG by Jay Alammar (Cohere) + Parameter-Efficient Fine-Tuning (PEFT)
|
|
RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Talk #0: Introduction by Chris Fregly (Principal SA, Generative AI) and Antje Barth (Principal Developer Advocate, Generative AI) Talk #1: Advanced Retrieval Augmented Generation (RAG) with Cohere on AWS by Jay Alammar, Director @ Cohere The rise of large language models is inspiring a wide variety of applications. Retrieval-Augmented Generation (RAG) stands as one of the leading use cases for developers and enterprises alike who need to empower the conversational ability of an LLM with grounding on specific data. This talk takes you beyond basic search-then-generate RAG to an advanced setup that utilizes reranking, query re-writing, and citations that vastly improve RAG systems. Talk #2: Parameter-Efficient Fine-Tuning (PEFT) by Shelbee Eigenbrode (Principal SA, Generative AI) RSVP Webinar: https://www.eventbrite.com/e/webinar-generative-ai-on-aws-tickets-45852865154 Zoom link: https://us02web.zoom.us/j/82308186562 Related Links Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm O'Reilly Book: https://www.amazon.com/Generative-AWS-Context-Aware-Multimodal-Applications/dp/1098159225 Website: https://generativeaionaws.com Meetup: https://meetup.generativeaionaws.com GitHub Repo: https://github.com/generative-ai-on-aws/ YouTube: https://youtube.generativeaionaws.com |
Advanced RAG by Jay Alammar (Cohere) + Parameter-Efficient Fine-Tuning (PEFT)
|
|
Generative AI on AWS
2023-11-13
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock |
O'Reilly AI & ML Books
|