March 21, 2024

Data Modeling in Motion: A live coding session for the mobility sector

10AM ET l 3PM CET l 2PM GMT With its document-oriented data model, real-time analytics capabilities, and seamless integration with other cutting-edge technologies, MongoDB has become the go-to choice for forward-thinking automakers and suppliers. As the industry continues to evolve and embrace NoSQL as more than just a database but as a new way of thinking about data, MongoDB’s role in fueling the future of smart mobility is set to expand. This session is designed to introduce developers, technologists, and technical decision-makers to the NoSQL mindset and to break down NoSQL data modeling for real-world workloads, deep diving into the core value of MongoDB for both SDEs and product stakeholders. Our live coding exploration will be guided by Rick Houlihan, who prior to joining MongoDB led the NoSQL Blackbelt team for Amazon during their migration from RDBMS to NoSQL, and Dr. Humza Akhtar, MongoDB’s principal of automotive manufacturing who brings extensive expertise in enabling Industry 4.0 and smart mobility solutions. We’ll cover: How to structure connected vehicle signal data with COVESA Vehicle Signal Specification (VSS), a natural NoSQL structure. Using VSS to store vehicle signals and user preferences in MongoDB Extending the VSS data model with an example Fleet Management schema to have a signal-to-fleet understanding of every vehicle using MongoDB Finally, using MongoDB's aggregation framework to generate a complete picture of fleet management operations A presentation recording will be shared with all registrants as well as the opportunity to book an exclusive one-on-one schema design review session with MongoDB NoSQL experts.

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March 14, 2024

Build smart applications with MongoDB Atlas and Google Cloud Vertex AI

10am - 11am GMT The application development landscape is evolving very rapidly. Today, users crave intuitive, context-aware experiences that understand their intent and deliver relevant results even when queries aren't perfectly phrased, putting an end to the keyword-based search practices. This is where MongoDB Atlas and Google Cloud Vertex AI can help users build and deploy scalable and resilient applications. MongoDB Atlas Vector Search is a cutting-edge tool that indexes and stores high-dimensional vectors, representing the essence of your data. It allows you to perform lightning-fast similarity searches, retrieving results based on meaning and context. Google Vertex AI is a comprehensive AI platform that houses an abundance of pre-trained models and tools, including the powerful Vertex AI PALM. This language model excels at extracting semantic representations from text data, generating those crucial vectors that fuel MongoDB Atlas Vector Search. Vector Search can be useful in a variety of contexts, such as natural language processing and recommendation systems. It is a powerful technique that can be used to find similar data based on its meaning. Join this webinar to learn more about the integration of MongoDB Atlas Vector Search with Google Cloud Vertex AI. We’ll cover: MongoDB & Google Cloud: Powering the next generation of GenAI applications Deep dive into the unified ML Platform and flexible data model Live demo The webinar will be recorded and all registrants will receive the recording. See you soon!

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February 28, 2024

MongoDB and Azure : Recipe for robust GenAI applications

10am-11am GMT It is no secret that Microsoft Azure OpenA I is one of the pioneers in the GenAI landscape. Coming from here, how do we then leverage Microsoft’s expertise to enable our customers to explore more GenAI opportunities and drive innovation with Atlas vector search? MongoDB Atlas Vector Search is built on MongoDB Developer Data Platform and allows customers to store vector embeddings along with their operational data, accessed using the uniform MongoDB Query API. Azure Open AI provides industry leading LLMs along with the enterprise security, compliance and regional availability inherent to Azure. Retrieval Augmented Generation (RAG) is a groundbreaking AI framework for improving the quality of responses from the Large Language Models (LLMs) by integrating external knowledge sources. But a robust RAG architecture requires carefully choosing the building blocks in RAG which is the Vector Database and the LLM. MongoDB’s Atlas Vector Search and Azure Open AI form a great better together story for innovative RAG implementations. In this webinar, we will explore how the collaboration works based on our own Production success story: MongoDB Atlas Vector Search and Azure Open AI: The integration RAG using AVS and AOAI - A Production Success Story Semantic Kernel for MongoDB Azure AI Studio - Integrate using Atlas Data Federation and Online Archive The session will be recorded and the recording will be sent to all registrants. See you soon!

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