AI is all the rage these days, but how can you make practical use of it without spending months of time learning this new technology? This session explains how to build an AI-powered content search tool for your own content in an afternoon, with a useful AI development pattern called retrieval augmented generation (RAG). We will demonstrate an updated version of the Docs Agent project that uses the Gemini API.

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This presentation explores deploying retrieval augmented generation (RAG) on Vertex AI Search to enhance QAD's internal data search (Jira, Confluence, Google Sites). Discover how GenAI improves query responses, utilizing a user-friendly web app on Google App Engine to counteract the loss of institutional knowledge. Join us for insights into this innovative enterprise search solution. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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The advent of Generative AI has ushered in an unprecedented era of innovation, marked by the transformative potential of Large Language Models (LLMs). The immense capabilities of LLMs open up vast possibilities for revolutionizing business ops and customer interactions. However, integrating them into production environments presents unique orchestration challenges. Successful orchestration of LLMs for Retrieval Augmented Generation (RAG) depends on addressing statelessness and providing access to the most relevant, up-to-date information. This session will dive into how to leverage LangChain and Google Cloud Databases to build context-aware applications that harness the power of LLMs. Please note: seating is limited and on a first-come, first served basis; standing areas are available

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RAG (retrieval-augmented generation) systems ground generative AI models in your own data, ensuring factuality, relevance, and control in the performance of your enterprise applications. However, building a RAG system from scratch is complex. In this session we'll share how Vertex AI Search can serve as an out-of-the-box RAG system for enterprise applications, handling data management, embedding, indexing, and retrieval with minimal setup time. Follow along to see how you can build and deploy a RAG-powered app in minutes and hours.

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Large Language Models (LLMs) have changed the way we interact with information. A base LLM is only aware of the information it was trained on. Retrieval augmented generation (RAG) can address this issue by providing context of additional data sources. In this session, we’ll build a RAG-based LLM application that incorporates external data sources to augment an OSS LLM. We’ll show how to scale the workload with distributed kubernetes compute, and showcase a chatbot agent that gives factual answers.

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Discover how to easily integrate Google-quality search in all customer and employee experiences. With Vertex AI, organizations can ground their generative AI applications with enterprise data, providing advanced RAG Search functionality and low total cost of ownership.

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Are LLMs useful for enterprises? Well, what is the use of a large language model that is trained on trillions of tokens but knows little to nothing about your business.

To make LLMs actually useful for enterprises, it is important for them to retrieve company's data effectively. LlamaIndex has been at the forefront of providing such solutions and frameworks to augment LLMs.

In this episode, Jerry Liu, Co-founder and CEO of LlamaIndex, joins Raja Iqbal, CEO and Chief Data Scientist at Data Science Dojo, for a deep dive into the intersection of generative AI, data. and entrepreneurship.

Jerry walks us through the cutting-edge technologies reshaping the generative AI landscape such as LlamaIndex. He also explores Retrieval Augmented Generation (RAG) and fine-tuning in detail, discussing their benefits, trade-offs, use cases, and enterprise adoption, making these complex tools and topics not just easily understandable but also fascinating.

Jerry further ventures into the heart of entrepreneurship, sharing valuable lessons and insights learned along his journey, from navigating his corporate career at tech giants like Apple, Quora, Two Sigma, and Uber, to starting as a founder in the data and AI landscape.

Amidst the excitement of innovation, Raja and Jerry also address the potential risks and considerations with generative AI. They raise thought-provoking questions about its impact on society, for instance, whether we're trading critical thinking for convenience.

Whether you're a generative AI enthusiast, seasoned entrepreneur, or simply curious about the future, this podcast promises plenty of knowledge and insights for you.

40 minutes + 5 mins questions. À travers des cas d'exemples (notamment des sites automatisés) et des retours concrets (RAG, plateformes...) qu'avons-nous appris quant à la réalisation d'un projet utilisant de l'IA générative ? Comment ces outils d'IA génératives vont impacter nos métiers.... Même les plus créatifs !

In this talk, we will discuss how AI-native databases productionize tasks like better search and integration with generative AI models for RAG, and multi-modal operations. You will learn how to achieve data isolation, redundancy and scalability for your AI-powered apps through features like multi-tenancy, replication, and horizontal scaling. There will be live demos and examples, of course. Join us to learn why an AI-native database should be an integral part of your AI tech stack in production.

In this talk, James Bentley (Awin), shares his story of going from zero knowledge of python to building RAG tools, chatbots, and even multi-modal AI prototypes in under a year by using GPT4 as a coding assistant. He shares his ups, his downs, and tips on how to move fast, and not break too many things. He will also give his first ever live demo of a project.

Send us a text Understanding Search, GenAI, RAG methodology, and vector databases with Nixon Cheaz, Engineering Lead at IBM's Experience Engine.   02:24 Meet Nixon Cheaz04:32 Search without Google06:35 Experience Engine08:30 Elements of Good Search12:46 Search Data Source15:36 GenAI Use Cases and Vector DBs 19:40 Foundational Models?22:07 Impact of Vector DBs25:38 IBM Public Content DB28:02 Use Cases29:58 IBM Technologies32:54 RAG40:12 Health is WealthLinkedIn: linkedin.com/in/nixon-cheaz

Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text More on GenAI, Hallucinations, RAG, Use Cases, LLMs, SLMs and costs with Armand Ruiz, Director watsonx Client Engineering and John Webb, Principal Client Engineering.  With this and the previous episode you'll be wiser on AI than 98% of the world.

00:12 Hallucinations02:33 RAG Differentiation06:41 Why IBM in AI09:23 Use Cases11:02 The GenAI Resume13:37 watson.x 15:40 LLMs17:51 Experience Counts20:03 AI that Surprises23:46 AI Skills26:47 Switching LLMs27:13 The Cost and SLMs28:21 Prompt Engineering29:16 For FunLinkedIn: linkedin.com/in/armand-ruiz, linkedin.com/in/john-webb-686136127 Website: https://www.ibm.com/client-engineering

Love what you're hearing? Don't forget to rate us on your favorite platform! Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text Let's go deep on GenAI, Foundational Models, and LLMs with Armand Ruiz, Director watsonx Client Engineering and John Webb, Principal Client Engineering.  Get a candid view on what is happening in the industy today. 01:40 Meet Armand Ruiz05:09 Meet John Webb06:43 The Client Engineering Practice07:51 GenAI11:50 IBM's AI Approach 13:50 GenAi in the Enterprise 15:47 Where to Start?18:10 RAG Method19:11 IBM's Differentiation21:25 AI Regulation24:22 LLM versus Smaller ModelsLinkedIn: linkedin.com/in/armand-ruiz, linkedin.com/in/john-webb-686136127 Website: https://www.ibm.com/client-engineering Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

We talked about:

Atita’s background How NLP relates to search Atita’s experience with Lucidworks and OpenSource Connections Atita’s experience with Qdrant and vector databases Utilizing vector search Major changes to search Atita has noticed throughout her career RAG (Retrieval-Augmented Generation) Building a chatbot out of transcripts with LLMs Ingesting the data and evaluating the results Keeping humans in the loop Application of vector databases for machine learning Collaborative filtering Atita’s resource recommendations

Links:

LinkedIn: https://www.linkedin.com/in/atitaarora/
Twitter: https://x.com/atitaarora Github: https://github.com/atarora Human-in-the-Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning Relevant Search: https://www.manning.com/books/relevant-search Let's learn about Vectors: https://hub.superlinked.com/ Langchain: https://python.langchain.com/docs/get_started/introduction Qdrant blog: https://blog.qdrant.tech/ OpenSource Connections Blog: https://opensourceconnections.com/blog/

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Intro on RAG and demo how RAG functions in the new Azure AI Studio.

In this talk, we are going to explore how vector databases like Weaviate can be used to build the next level of RAG applications. Based on examples we are looking at the possibilities of RAG architectures that can handle multimodal context like images, videos and much more and how vector databases work in such a setting

Send us a text Back to talking Data with Ed Anuff, CPO, DataStax.  With experience at Google, Apigee, Six Apart, Vignette, Epicentric, and Wired, Ed talks the future of databases with AI and GenAI.  

05:04 The Crazy life of Ed Anuff08:12 DataStax defined10:06 Vector Database11:58 GenAI and RAG Pattern18:03 DataStax Differentiation21:39 NoSQL vs SQL24:27 Common AI Use Cases25:47 The Secret to ChatGPT31:10 DataStax 2min Pitch31:42 The Future35:47 Bring AI to the DataLinkedIn: linkedin.com/in/edanuff Website: https://www.datastax.com/ Want to be featured as a guest on Making Data Simple?  Reach out to us at  [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

From Chat GPT to the NBA to Mercedes-Benz, Azure Cosmos DB is enabling intelligent apps that change the way we live and work. Join us to learn from KPMG about how they built a generative AI-based assistant, and Bond Brand Loyalty on how they scale data to meet global customer demand, with Azure Cosmos DB. We'll explore capabilities like vector search and how to implement RAG pattern, along with improved elasticity, and greater scale.

To learn more, please check out these resources: * https://aka.ms/Ignite23CollectionsBRK226H * https://info.microsoft.com/ww-landing-contact-me-for-events-m365-in-person-events.html?LCID=en-us&ls=407628-contactme-formfill * https://aka.ms/azure-ignite2023-dataaiblog

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * James Codella * Kirill Gavrylyuk * Maria Pallante * Mark Brown * Robert Finlayson * Anitha Adusumilli * Estefani Arroyo * Andrew Liu * Marko Hotti * Rodrigo Souza * Jason Fogaty

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This video is one of many sessions delivered for the Microsoft Ignite 2023 event. View sessions on-demand and learn more about Microsoft Ignite at https://ignite.microsoft.com

BRK226HG | English (US) | Data

MSIgnite

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