Harnessing the Power of Vectors and AI to Build the Next Generation of Intelligent Apps
A new wave of AI has emerged, changing the way businesses use internal and external data in existing workflows. The combination of Large Language Models (LLMs) and Embedding Models (models that can create high dimensional vectors from unstructured data) now enable you to make sense of data of any type. With >80% of information being unstructured (think text, documents, images, video files, etc.), search is moving beyond just the keyword, with vector embeddings helping to contextualize all of this data, even when your end users might not know what they’re looking for. So how can your business utilize vector search and the power of LLMs to extend your own corporate knowledge set and increase relevant results? Join our expert Rashi Yadav, Solutions Architect at MongoDB for a discussion on these tectonic trends to find out What Vector Search is and how AI plays a role in making sense of unstructured data. How to create vector embeddings to increase relevance by harnessing the power of LLMs. Various approaches to storing and retrieving vectors. Real world examples of vector use cases, AI integrations, and results. Signup to watch this on-demand session
How mature is your ecommerce search – are you ready for AI?
Are the products that your customers need as easy to find in your ecommerce store as your competitors? In today’s full tilt pace of online and mobile shopping, flawless search experiences can make or break the connection between looking and buying. Join industry search experts, Erik Hatcher, Staff Developer Advocate at MongoDB, and Phil Lewis, co-founder and CTO at Pureinsights, to discover: What is the Search Maturity Matrix, and which capabilities are your organization missing to achieve better results How retailers are building smarter search applications with AI What’s possible with MongoDB’s new Vector Search offering
Back to Basics with MongoDB Atlas
Sign up to learn the fundamentals of the document database and why they have become the widely used alternative to traditional relational databases, with direct access to a MongoDB expert who will help you make the most of MongoDB. In Back to Basics webinar, we'll introduce you to the foundational concepts of the world's most popular NoSQL database, MongoDB and explain how you can leverage the MongoDB Cloud to build modern, data-driven applications.
Atlas 101 eWorkshop (English)
Watch this exclusive workshop and get a full technical overview and demo of MongoDB Atlas followed by hands on exercises and direct access to our expert panel. During this time, you will learn how to: Create a database cluster using MongoDB Atlas Secure the database & Load sample data Create indexes and leverage them in your aggregation pipelines using Atlas Get started with MongoDB Charts and create visual representations of your data How to use Atlas Vector Search and AI for context aware search
Generative AI with MongoDB Atlas and BigQuery: A Developer’s Guide
Watch our webinar where you’ll learn how to leverage MongoDB Atlas on Google Cloud (GC) BigQuery for generative natural language tasks. Our seasoned developer advocates will walk you through a practical use case, step-by-step, covering: Moving subsets of operational MongoDB Atlas data into BigQuery. Creating machine learning models in BigQuery ML. Using Generative AI models to perform generative natural language tasks, such as generating text, translating languages, and writing different kinds of creative content.
Building an Industrial Unified Namespace with HiveMQ and MongoDB
As factories become more connected and data-driven, it is essential to have a unified and standardized approach to manufacturing data management. Industrial Unified Namespace (IUN) follows an event-driven architecture topology where different manufacturing applications publish events and context in real-time to a central data repository. This results in a decoupled ecosystem, allowing applications and services to provide and consume data when and where needed. This on-demand webinar delves into the powerful synergy of HiveMQ and MongoDB, showcasing how these technologies collaboratively help customers construct a scalable and flexible Industrial Unified Namespace. We’ll share: The significance of an IUN in Industry 4.0 architectures How HiveMQ and MongoDB play in the manufacturing sector Real-world industrial use cases
Build Your AI Roadmap for 2024
AI tech and tools have transformed how companies shape priorities and ship products. To better understand the extent of that impact, Retool recently surveyed engineering leads, founders, and developers to find out how teams are leveraging AI tools and what role AI will play in their engineering teams. MongoDB Director of Product Management, Rachelle Palmer, joined Cory Wilkerson, Engineering Lead at Retool to discuss how to successfully build and ship AI features. In the session, they covered the evolving universe of use cases and how organizations are making changes to support them. Topics included: Key learnings and takeaways from Retool’s 2023 State of the AI report Why and how to build AI features How to ship AI-powered apps that combine LLMs with your own data using MongoDB Atlas Vector Search and RAG Guide for implementing AI into your 2024 roadmap
Atlas 201 eWorkshop (English)
Watch this exclusive workshop to get a full overview on how to migrate from a relational database to MongoDB followed by hands-on exercises and direct access to our expert panel. During this time, you will learn how to: Migrate from a relational databases to MongoDB How to build design patterns using MongoDB How to create aggregation pipelines How to use Atlas Vector Search and AI for context aware search
Modernize Your Apps with MongoDB Relational Migrator Webinar Hong Kong
As more and more organizations seek to improve performance, scalability, and developer productivity, many are turning to MongoDB as a solution. However, migrating legacy apps from relational databases to MongoDB can be a daunting task. That's where MongoDB Relational Migrator comes in. In this session, we explored how Relational Migrator can help reduce risk and effort throughout the various stages of a migration project, including data modeling, data migration, and application code modernization. Language: Cantonese
Building Next Generation Applications Using Generative AI
With everyone jumping on the generative AI bandwagon, the real competitive advantage will come from businesses using their own data in conjunction with Large Language Models (LLMs). But doing so will require new skills. And it will no doubt usher in new concerns about data security and governance. MongoDB Senior Vice President Product Management, Andrew Davidson, was joined by Fern Halper, VP and Sr. Research Director for Advanced Analytics at TDWI for an informal chat about building and deploying generative AI applications. The discussion was followed by a live Q&A. Topics included: An overview of generative AI and the state of market adoption Use cases that leverage a company’s internal data The importance of vector databases in generative AI How to build and deploy a generative AI application while protecting company data Modern platforms to support AI and generative AI
Why componentization is essential in a competitive market with MongoDB, Capgemini and Temenos
In this episode of FF Virtual Arena, Fintech Finance hosted Temenos, MongoDB, and Capgemini to find out why componentization and tech partnerships are essential today. This session brings together industry experts who have worked with the best in banking space to help them innovate and leverage the advantages of componentization. Some of the key takeaways discussions include: Adding components is not just about upgrading legacy systems but also finding new revenue streams Banks are increasingly looking at moving data around and leveraging rich data to deliver a better service How can you access data in real time and get a holistic view of customers What the impact of AI is on componentization Speakers: Doug Mackenzie, Chief Content Officer, Fintech Finance News Joerg Schmuecker: Managing Director, Financial Services and Insurance Industry Solutions at MongoDB Mark Ashton: Senior Director, Cloud Practice Lead and Enterprise Architect at Capgemini Financial Services Paul Carr: Global Head of Partner Ecosystem at Temenos