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Title & Speakers	Event
Scaling Context Engineering and Data Management for A 2025-12-18 · 22:00 Topic: Scaling Context Engineering and Data Management for AI Description: Building AI applications today requires managing massive data scale while maintaining high output accuracy. Context engineering, especially retrieval-augmented generation (RAG) with vector search, is a powerful way to improve AI reliability. But as datasets and vector volumes grow, costs often escalate and force trade-offs between accuracy and efficiency. Hybrid search eliminates this compromise. In this webinar, you will learn about the new hybrid search capabilities introduced in Doris 4.0 and how a major global tech company leveraged Apache Doris to achieve superior accuracy while keeping infrastructure costs under control. Agenda Quick recap of whats new in Apache Doris 4.0 VeloDB Cloud Doris 4.0 Tech Preview announcement and how to access it Real-world example: Hybrid search and analytics processing use case Q&A Speak with Our Knowledgeable Advisor Access Our Complimentary Career Guide Transform Your Career with Us in Just 14 Weeks Discover More About WeCloudData ABOUT US WeCloudData is the leading accredited education institute in North America that focuses on Data Science, Data Engineering, DevOps, Artificial Intelligence, and Business Intelligence. Developed by industry experts, and hiring managers, and highly recognized by our hiring partners, WeCloudData’s learning paths have helped many students make successful transitions into data and DevOps roles that fit their backgrounds and passions. WeCloudData provides a different and more practical teaching methodology, so that students not only learn the technical skills but also acquire the soft skills that will make them stand out in a work environment. WeCloudData has also partnered with many big companies to help them adopt the latest tech in Data, AI, and DevOps. Visit our website for more information: https://weclouddata.com	Scaling Context Engineering and Data Management for A
Context Engineering for Multi-Agent Systems 2025-11-18 Denis Rothman – author Build AI that thinks in context using semantic blueprints, multi-agent orchestration, memory, RAG pipelines, and safeguards to create your own Context Engine Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Design semantic blueprints to give AI structured, goal-driven contextual awareness Orchestrate multi-agent workflows with MCP for adaptable, context-rich reasoning Engineer a glass-box Context Engine with high-fidelity RAG, trust, and safeguards Book Description Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios. Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence. By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence. Email sign-up and proof of purchase required What you will learn Develop memory models to retain short-term and cross-session context Craft semantic blueprints and drive multi-agent orchestration with MCP Implement high-fidelity RAG pipelines with verifiable citations Apply safeguards against prompt injection and data poisoning Enforce moderation and policy-driven control in AI workflows Repurpose the Context Engine across legal, marketing, and beyond Deploy a scalable, observable Context Engine in production Who this book is for This book is for AI engineers, software developers, system architects, and data scientists who want to move beyond ad hoc prompting and learn how to design structured, transparent, and context-aware AI systems. It will also appeal to ML engineers and solutions architects with basic familiarity with LLMs who are eager to understand how to orchestrate agents, integrate memory and retrieval, and enforce safeguards. software-development software-architecture architectural-patterns service-oriented-architecture-soa service-oriented architecture (soa) AI/ML GenAI LLM Marketing RAG	O'Reilly Data Engineering Books
RAGChat: User login and data access control 2025-01-27 · 23:30 In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat In our RAG flow, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2. This session is a part of a series. To learn more, click here	RAGChat: User login and data access control
RAG with Data Access Control 2024-09-11 · 20:00 If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2. Presented by Matt Gotteiner, Product Manager for Azure AI Search, and Pamela Fox, Developer Advocate for Python Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack 📌 Check out the RAGHack 2024 series here! Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24. Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!	RAG with Data Access Control
Data Access Control for AI RAG Apps on Azure 2024-07-17 · 19:00 If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2. Part of our Reactor series on Securing AI Apps on Azure!	Data Access Control for AI RAG Apps on Azure
Data Access Control for AI RAG Apps on Azure 2024-07-17 · 19:00 If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2. Part of our Reactor series on Securing AI Apps on Azure!	Data Access Control for AI RAG Apps on Azure
Azure Developers JavaScript Day 2024-03-06 · 17:00 About the Event: Join us for a 2-day virtual conference on March 6 & 7 to discover the latest services and features in Azure designed specifically for JavaScript developers. You'll hear directly from the experts behind the most sought-after cloud services for developers to learn cutting-edge cloud development techniques that can save you time and money, while providing your customers with the best experience possible. Join us for our Cloud Skills Challenge at https://aka.ms/AzureJSDay-Challenge. Who Should Attend: This event is for everyone interested in learning about JavaScript in Azure and all the opportunities you can unlock, regardless of your level of experience with Azure or JavaScript! Agenda: DAY 1: BUILDING AN INTELLIGENT APP WITH JS: DEV TOOLS AND AI 9:00 - 9:10 Introduction and Setup (10m) - Natalia Venditto, Principal Product Owner JavaScript E2E DevEx; Dina Berry, Senior Content Developer Azure JavaScript DevEx Welcome to the start of the day where we will go over the agenda and then get set up with forking the repo we will use for this event. 9:10 - 9:40 GitHub Copilot Can Do That? (30 m) - Burke Holland, Principal Cloud Advocate VS Code It’s hard to go even a single day anymore without hearing the word “Copilot”. GitHub Copilot is the original Copilot and the most widely adopted AI tool in history. In this session, we’ll jump into GitHub Copilot and take a look at some of the astonishing things that it can do and how it can make your life as a developer exponentially easier and more enjoyable. 9:40 - 10:25 Building a versatile RAG Pattern chat bot with Azure OpenAI, LangChain (45m) - Wassim Chegham, Senior Software Engineer JavaScript Developer Advocacy; Natalia Venditto, Principal Product Owner JavaScript E2E DevEx; Lars Gyrup Brink Nielsen, Microsoft MVP In this session we will walk you through the code of our popular JavaScript Azure OpenAI sample, from the backend services, to the frontend application, and even the schema that connects them seamlessly together: the Chat Application Protocol. Lars will also present the most cutting edge new features of Angular in its version 17, a favorite to build enterprise scale applications with! 10:25 - 10:45 LangChain.js + Azure: A Generative AI App Journey (20m) - Yohan Lasorsa, Senior JavaScript Developer Advocate Discover the journey of building a generative AI application using LangChain.js and Azure. This talk will explore the development process from idea to production, focusing on a RAG-based approach for a Q&A system using YouTube video transcripts. We'll demonstrate how we built a local prototype using open-source models and Ollama, and its transition to Azure for production. 10:45 - 11:10 Extending Copilot for Microsoft 365 using JavaScript and TypeScript (25m) - Bob German, Principal Cloud Advocate Microsoft 365 You may have heard that Microsoft 365 now has an AI Copilot to help users do more within Microsoft 365. What you might not know is that you can extend Copilot to work with your business data and external content. In this session you’ll learn how to extend Copilot with plugins and Graph connectors written in JavaScript and TypeScript. We’ll examine the architecture, relevant Azure resources, and of course the code. All code will be made available so you can try it yourself. It’s easy – please join the session to get started! 11:10 – 11:30 Have a safe coffee chat with your documentation using Azure AI Services (20m) - Maya Shavin, Senior Software Engineer Building a custom documentation assistant using AI has become achievable with the help of GPT, LangChain and other AI tools. But how can we control the content quality of the coffee chat made to our document assistant, from the user to the assistant’s response? What options do we have to enhance the content safety in our question-and-answer flow, while scaling our project to handle further scenarios? Join my talk and let’s find out. 11:30-11:40 Outro (10m) DAY 2: BUILDING AN INTELLIGENT APP WITH JS: HOSTING AND INTEGRATIONS 9:00 - 9:10 Introduction (10m) – Natalia Venditto, Principal Product Owner JavaScript E2E DevEx, Welcome to the start of day two where we will go over today’s agenda and recap yesterday’s event. 9:10 - 9:40 Crafting Future-proof Apps with JavaScript & Azure Cosmos DB (30m) - Sajeetharan Sinnathurai, Principal Product Manager Azure Cosmos DB In this session, we'll discuss the developer experience of Cosmos DB with JavaScript, covering the latest additions to the SDK. Additionally, we'll explore Vercel integration for seamless deployment of JavaScript-based applications using templates. 9:40 - 10:00 Turn your database into GraphQL APIs with Azure Static Web Apps Database Connections (20m) - Thomas Gavin, Product Manager Azure Static Web Apps; Stacy Cashmore, MVP for Developer Technologies Skip the boilerplate server code and use Static Web Apps Database Connections to directly access your database contents using a set of provided GraphQL APIs. In this session, we demo how you can quickly go from frontend to full-stack, saving results in a CosmosDB database using Database Connections and deploying to Azure Static Web Apps. 10:00 – 10:25 Build real-time web apps with Socket.IO and let Azure handle scalability, no more adapters (25m) - Ken Chen, Principal Software Eng Manager Azure Web PubSub and Azure SignalR; Kevin Guo, Senior Product manager Azure Web PubSub and Azure SignalR Socket.IO is a popular open-source library among JavaScript developers for building real-time web apps. In this session, we are going to explore what we mean by “real-time” web apps and how Socket.IO library can help web developers build them. Also, we discuss a common challenge among Socket.IO developers – scaling out to multiple Socket.IO servers. Through a quick demo, we showcase how easy it is to leverage the recently introduced support for Socket.IO on Azure to offload scalability issue to a cloud service. 10:25 - 11:10 Playwright in Action: From Setup to Best Practices (45m) - Max Schmitt, Software Engineer Playwright; Stefan Judis, Playwright Ambassador Dive into the essentials of end-to-end testing with Playwright in this engaging 45-minute session. A Playwright core contributor will guide you through a hands-on demo, demonstrating how to efficiently set up, execute automated tests and debug them in GitHub Actions. After that a Playwright ambassador will share the best practices and tips to optimize your testing workflow. 11:10 – 11:20 Outro (10m) This is a great chance to start or advance your journey towards improving your developer productivity and innovation. Join us for exciting sessions with insights, useful tips, and interactive discussions that will help you unlock your full potential as a JavaScript developer. We can't wait to see you there on March 6 & 7!	Azure Developers JavaScript Day
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01YzHZw0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01_Yrgb0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01YzHZw0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application
Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00 To access this webinar, please register here: https://hubs.li/Q01_8X6V0 Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application” Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges. Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes. Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives. Abstract: Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work. We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs. Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q01YzHZw0 • Code of conduct: https://odsc.com/code-of-conduct/	Preparing for your First Enterprise Large Language Model (LLM) Application

Scaling Context Engineering and Data Management for A 2025-12-18 · 22:00

Topic: Scaling Context Engineering and Data Management for AI

Description: Building AI applications today requires managing massive data scale while maintaining high output accuracy. Context engineering, especially retrieval-augmented generation (RAG) with vector search, is a powerful way to improve AI reliability. But as datasets and vector volumes grow, costs often escalate and force trade-offs between accuracy and efficiency. Hybrid search eliminates this compromise.

In this webinar, you will learn about the new hybrid search capabilities introduced in Doris 4.0 and how a major global tech company leveraged Apache Doris to achieve superior accuracy while keeping infrastructure costs under control.

Agenda

Quick recap of whats new in Apache Doris 4.0
VeloDB Cloud Doris 4.0 Tech Preview announcement and how to access it
Real-world example: Hybrid search and analytics processing use case
Q&A

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Developed by industry experts, and hiring managers, and highly recognized by our hiring partners, WeCloudData’s learning paths have helped many students make successful transitions into data and DevOps roles that fit their backgrounds and passions. WeCloudData provides a different and more practical teaching methodology, so that students not only learn the technical skills but also acquire the soft skills that will make them stand out in a work environment.

WeCloudData has also partnered with many big companies to help them adopt the latest tech in Data, AI, and DevOps. Visit our website for more information: https://weclouddata.com

Scaling Context Engineering and Data Management for A

Context Engineering for Multi-Agent Systems 2025-11-18

Denis Rothman – author

Build AI that thinks in context using semantic blueprints, multi-agent orchestration, memory, RAG pipelines, and safeguards to create your own Context Engine Free with your book: DRM-free PDF version + access to Packt's next-gen Reader Key Features Design semantic blueprints to give AI structured, goal-driven contextual awareness Orchestrate multi-agent workflows with MCP for adaptable, context-rich reasoning Engineer a glass-box Context Engine with high-fidelity RAG, trust, and safeguards Book Description Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios. Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence. By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence. Email sign-up and proof of purchase required What you will learn Develop memory models to retain short-term and cross-session context Craft semantic blueprints and drive multi-agent orchestration with MCP Implement high-fidelity RAG pipelines with verifiable citations Apply safeguards against prompt injection and data poisoning Enforce moderation and policy-driven control in AI workflows Repurpose the Context Engine across legal, marketing, and beyond Deploy a scalable, observable Context Engine in production Who this book is for This book is for AI engineers, software developers, system architects, and data scientists who want to move beyond ad hoc prompting and learn how to design structured, transparent, and context-aware AI systems. It will also appeal to ML engineers and solutions architects with basic familiarity with LLMs who are eager to understand how to orchestrate agents, integrate memory and retrieval, and enforce safeguards.

software-development software-architecture architectural-patterns service-oriented-architecture-soa service-oriented architecture (soa) AI/ML GenAI LLM Marketing RAG

O'Reilly Data Engineering Books

RAGChat: User login and data access control 2025-01-27 · 23:30

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat

In our RAG flow, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2.

This session is a part of a series. To learn more, click here

RAGChat: User login and data access control

RAG with Data Access Control 2024-09-11 · 20:00

If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2.

Presented by Matt Gotteiner, Product Manager for Azure AI Search, and Pamela Fox, Developer Advocate for Python

** Part of RAGHack, a free global hackathon to develop RAG applications. Join at https://aka.ms/raghack **

📌 Check out the RAGHack 2024 series here!

Pre-requisites: - Read the official rules and join the hack at https://aka.ms/raghack. No Purchase Necessary. Must be 18+ to enter. Contest ends 9/16/24.

Want more hands-on RAG training? Visit the Reactor series home page to see all the RAGHack 2024 sessions!

RAG with Data Access Control

Data Access Control for AI RAG Apps on Azure 2024-07-17 · 19:00

If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2.

Part of our Reactor series on Securing AI Apps on Azure!

Data Access Control for AI RAG Apps on Azure

Data Access Control for AI RAG Apps on Azure 2024-07-17 · 19:00

If you're trying to get an LLM to accurately answer questions about your own documents, you need RAG: Retrieval Augmented Generation. With a RAG approach, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2.

Part of our Reactor series on Securing AI Apps on Azure!

Data Access Control for AI RAG Apps on Azure

Azure Developers JavaScript Day 2024-03-06 · 17:00

About the Event:

Join us for a 2-day virtual conference on March 6 & 7 to discover the latest services and features in Azure designed specifically for JavaScript developers. You'll hear directly from the experts behind the most sought-after cloud services for developers to learn cutting-edge cloud development techniques that can save you time and money, while providing your customers with the best experience possible.

Join us for our Cloud Skills Challenge at https://aka.ms/AzureJSDay-Challenge.

Who Should Attend:

This event is for everyone interested in learning about JavaScript in Azure and all the opportunities you can unlock, regardless of your level of experience with Azure or JavaScript!

Agenda:

DAY 1: BUILDING AN INTELLIGENT APP WITH JS: DEV TOOLS AND AI

9:00 - 9:10 Introduction and Setup (10m) - Natalia Venditto, Principal Product Owner JavaScript E2E DevEx; Dina Berry, Senior Content Developer Azure JavaScript DevEx

Welcome to the start of the day where we will go over the agenda and then get set up with forking the repo we will use for this event.

9:10 - 9:40 GitHub Copilot Can Do That? (30 m) - Burke Holland, Principal Cloud Advocate VS Code

It’s hard to go even a single day anymore without hearing the word “Copilot”. GitHub Copilot is the original Copilot and the most widely adopted AI tool in history. In this session, we’ll jump into GitHub Copilot and take a look at some of the astonishing things that it can do and how it can make your life as a developer exponentially easier and more enjoyable.

9:40 - 10:25 Building a versatile RAG Pattern chat bot with Azure OpenAI, LangChain (45m) - Wassim Chegham, Senior Software Engineer JavaScript Developer Advocacy; Natalia Venditto, Principal Product Owner JavaScript E2E DevEx; Lars Gyrup Brink Nielsen, Microsoft MVP

In this session we will walk you through the code of our popular JavaScript Azure OpenAI sample, from the backend services, to the frontend application, and even the schema that connects them seamlessly together: the Chat Application Protocol. Lars will also present the most cutting edge new features of Angular in its version 17, a favorite to build enterprise scale applications with!

10:25 - 10:45 LangChain.js + Azure: A Generative AI App Journey (20m) - Yohan Lasorsa, Senior JavaScript Developer Advocate

Discover the journey of building a generative AI application using LangChain.js and Azure. This talk will explore the development process from idea to production, focusing on a RAG-based approach for a Q&A system using YouTube video transcripts. We'll demonstrate how we built a local prototype using open-source models and Ollama, and its transition to Azure for production.

10:45 - 11:10 Extending Copilot for Microsoft 365 using JavaScript and TypeScript (25m) - Bob German, Principal Cloud Advocate Microsoft 365

You may have heard that Microsoft 365 now has an AI Copilot to help users do more within Microsoft 365. What you might not know is that you can extend Copilot to work with your business data and external content. In this session you’ll learn how to extend Copilot with plugins and Graph connectors written in JavaScript and TypeScript. We’ll examine the architecture, relevant Azure resources, and of course the code. All code will be made available so you can try it yourself. It’s easy – please join the session to get started!

11:10 – 11:30 Have a safe coffee chat with your documentation using Azure AI Services (20m) - Maya Shavin, Senior Software Engineer

Building a custom documentation assistant using AI has become achievable with the help of GPT, LangChain and other AI tools. But how can we control the content quality of the coffee chat made to our document assistant, from the user to the assistant’s response? What options do we have to enhance the content safety in our question-and-answer flow, while scaling our project to handle further scenarios? Join my talk and let’s find out.

11:30-11:40 Outro (10m)

DAY 2: BUILDING AN INTELLIGENT APP WITH JS: HOSTING AND INTEGRATIONS

9:00 - 9:10 Introduction (10m) – Natalia Venditto, Principal Product Owner JavaScript E2E DevEx,

Welcome to the start of day two where we will go over today’s agenda and recap yesterday’s event.

9:10 - 9:40 Crafting Future-proof Apps with JavaScript & Azure Cosmos DB (30m) - Sajeetharan Sinnathurai, Principal Product Manager Azure Cosmos DB

In this session, we'll discuss the developer experience of Cosmos DB with JavaScript, covering the latest additions to the SDK. Additionally, we'll explore Vercel integration for seamless deployment of JavaScript-based applications using templates.

9:40 - 10:00 Turn your database into GraphQL APIs with Azure Static Web Apps Database Connections (20m) - Thomas Gavin, Product Manager Azure Static Web Apps; Stacy Cashmore, MVP for Developer Technologies

Skip the boilerplate server code and use Static Web Apps Database Connections to directly access your database contents using a set of provided GraphQL APIs. In this session, we demo how you can quickly go from frontend to full-stack, saving results in a CosmosDB database using Database Connections and deploying to Azure Static Web Apps.

10:00 – 10:25 Build real-time web apps with Socket.IO and let Azure handle scalability, no more adapters (25m) - Ken Chen, Principal Software Eng Manager Azure Web PubSub and Azure SignalR; Kevin Guo, Senior Product manager Azure Web PubSub and Azure SignalR

Socket.IO is a popular open-source library among JavaScript developers for building real-time web apps. In this session, we are going to explore what we mean by “real-time” web apps and how Socket.IO library can help web developers build them. Also, we discuss a common challenge among Socket.IO developers – scaling out to multiple Socket.IO servers. Through a quick demo, we showcase how easy it is to leverage the recently introduced support for Socket.IO on Azure to offload scalability issue to a cloud service.

10:25 - 11:10 Playwright in Action: From Setup to Best Practices (45m) - Max Schmitt, Software Engineer Playwright; Stefan Judis, Playwright Ambassador

Dive into the essentials of end-to-end testing with Playwright in this engaging 45-minute session. A Playwright core contributor will guide you through a hands-on demo, demonstrating how to efficiently set up, execute automated tests and debug them in GitHub Actions. After that a Playwright ambassador will share the best practices and tips to optimize your testing workflow.

11:10 – 11:20 Outro (10m)

** This is a great chance to start or advance your journey towards improving your developer productivity and innovation. Join us for exciting sessions with insights, useful tips, and interactive discussions that will help you unlock your full potential as a JavaScript developer. We can't wait to see you there on March 6 & 7! **

Azure Developers JavaScript Day

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01_Yrgb0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01_Yrgb0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01YzHZw0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01_Yrgb0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01YzHZw0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

Preparing for your First Enterprise Large Language Model (LLM) Application 2023-08-31 · 16:00

To access this webinar, please register here: https://hubs.li/Q01_8X6V0

Topic: “Preparing for your First Enterprise Large Language Model (LLM) Application”

Speaker#1: Nicolas Decavel-Bueff, Data Science Consultant at Pandata

He is an SF-based Data Scientist who has delivered valuable solutions across a broad spectrum of industries. His accomplishments range from employing natural language processing models in logistics to leading teams in the development of vital models in the utility sector. His work with diverse tools has consistently created quantifiable business impact. Equipped with a Master's in Data Science from the University of San Francisco, Nicolas blends academic rigor and practical experience to address complex business challenges.

Speaker#2: Parham Parvizi, Founder of Data Stack Academy and Tura.io

Parham is a founding member of Tura.io and DataStack.Academy. Tura is a group of professional Cloud Data Engineers and Architects while Data Stack Academy is the most comprehensive Data Engineering bootcamp; training the future of Cloud Data Engineers. In his 20 years as Data Engineer and Cloud/Big Data Solution Architect, he has been an Apache Software Foundation contributor and an early adopter and contributor to open source Big Data projects as Map Reduce and Hive. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. Prior to Tura Labs, he was a product manager at Pivotal and one of the initial members of Talend. As a Data Advisor and consultant, Parham’s has had the opportunity and pleasure to work with nearly every fortune 100 company over the years. From managing thousands node clusters to optimizing data task that you are familiar with behind the scenes.

Speaker#3: Cal Al-Dhubaib, Founder & AI Strategist at Pandata

Cal is a globally recognized data scientist, entrepreneur, and innovator in responsible artificial intelligence, specializing in high-risk sectors such as healthcare, energy, and defense. He is the founder and CEO of Pandata, a consulting company that helps organizations to design and develop AI-driven solutions for complex business challenges, with an emphasis on responsible AI. Their clients include globally recognized organizations like the Cleveland Clinic, Progressive Insurance, University Hospitals, and Parker Hannifin. Cal frequently speaks on topics including AI ethics, change management, data literacy, and the unique challenges of implementing AI solutions in high-risk industries. His insights have been featured in noteworthy publications such as Forbes, Ohiox, the Marketing AI Institute, Open Data Science, and AI Business News. Cal has also received recognition among Crain’s Cleveland Notable Immigrant Leaders, Notable Entrepreneurs, and most recently, Notable Technology Executives.

Abstract:

Amid the growing accessibility of performant Large Language Models (LLMs) like GPT-4 and Llama-2, and a burgeoning range of commercial licenses, enterprises are now forging the first wave of LLM-driven applications. This session will begin by addressing the challenges with LLMs such as defining clear success criteria for an LLM project and understanding different approaches to LLM work.

We’ll further address technical challenges such as memory handling, input quality control ("garbage in, garbage out"), and the complexities of embeddings. A comparison of different approaches from using Retrieval-Augmented Generation (RAG) to training or fine-tuning on top of a variety of different LLMs.

Through real-world examples we will discuss practical approaches to risk management, ethical considerations, and the ideal team composition for an LLM project. In addition, we’ll discuss a variety of tools that form the modern LLM application development stack. Join us to demystify LLM applications and equip your organization with the knowledge to succeed.

ODSC Links:

• Get free access to more talks/trainings like this at Ai+ Training platform:

https://hubs.li/H0Zycsf0

• ODSC blog: https://opendatascience.com/

• Facebook: https://www.facebook.com/OPENDATASCI

• Twitter: https://twitter.com/_ODSC & @odsc

• LinkedIn: https://www.linkedin.com/company/open-data-science

• Slack Channel: https://hubs.li/Q01YzHZw0

• Code of conduct: https://odsc.com/code-of-conduct/

Preparing for your First Enterprise Large Language Model (LLM) Application

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