Resources

Power-Up Your Queries: MongoDB's Aggregation Framework

In this webinar, we'll show you how MongoDB's aggregation framework allows you to write simple, and then increasingly complex, queries and aggregations against your datasets, helping you group, join, limit, skip, and bucket across documents in your collections. We'll introduce some of the ideas around data modeling patterns and indexing that will help you get the most out of storing and querying your data in MongoDB, and you'll learn powerful tools for writing and testing your queries. If you're already working with MongoDB, and are familiar with the document model, CRUD and query operations, and you're looking to build more complex queries to aggregate and analyze data, then this is the perfect webinar for you. We’ll be exploring these topics through various languages. Pick the one that suits you best: Java - Tuesday, June 18th, 11 A.M. ET JavaScript - Wednesday, July 31st, 11 A.M. ET C#/.NET - Wednesday, September 4th, 11 A.M. ET Power-Up Your Queries in Python: On-Demand Don’t miss this opportunity to power-up your queries and get your questions answered. We look forward to seeing you there! Dates are subject to change

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June 19, 2024

MongoDB & Amazon Web Services (AWS) Fraud Detection Immersion Day (EMEA timezone)

10:00am-12:30pm BST | 11:00am-1:30pm CEST Not the right time zone? We are also running this on 3rd July in Asia-Pacific and 24th July in AMER . Existing fraud detection systems often grapple with a critical limitation – relying on stale data – which limits the ability to access data in real-time to prevent fraudulent activities before they happen. Join MongoDB and AWS for a deep dive virtual workshop into Fraud Detection using MongoDB Atlas and Amazon SageMaker Canvas to learn how you can proactively identify and combat fraudulent activities, safeguarding systems and protecting customers. By leveraging MongoDB Atlas as an operational data store you are able to accommodate high-volume transactional data with exceptional performance and flexibility. Coupled with Amazon SageMaker Canvas, leverage AI/ML solutions effortlessly with a no-code platform that brings the power of advanced analytics for fraud detection to your fingertips. In the virtual workshop, you will learn: How to architect, design, build, and deploy applications with MongoDB Atlas in the AWS Marketplace How to identify and prevent fraudulent activities or transactions with a modern data architecture Data analytics and machine learning techniques to identify patterns and anomalies How to monitor transactions in real-time, analyse historical data, and flag any suspicious activity with with MongoDB Atlas and Sagemaker Canvas You will hear from: Bhavik Bhatt: Associate Solutions Architect at MongoDB Igor Alekseev: Data & Analytics Partner Solutions Architect at AWS

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July 24, 2024

MongoDB & Amazon Web Services (AWS) Fraud Detection Immersion Day (AMER timezone)

12:00-2:30pm EDT Not the right time zone? We are also running this on 19th June in EMEA and 3rd July in Asia-Pacific . Existing fraud detection systems often grapple with a critical limitation – relying on stale data – which limits the ability to access data in real-time to prevent fraudulent activities before they happen. Join MongoDB and AWS for a deep dive virtual workshop into Fraud Detection using MongoDB Atlas and Amazon SageMaker Canvas to learn how you can proactively identify and combat fraudulent activities, safeguarding systems and protecting customers. By leveraging MongoDB Atlas as an operational data store you are able to accommodate high-volume transactional data with exceptional performance and flexibility. Coupled with Amazon SageMaker Canvas, leverage AI/ML solutions effortlessly with a no-code platform that brings the power of advanced analytics for fraud detection to your fingertips. In the virtual workshop, you will learn: How to architect, design, build, and deploy applications with MongoDB Atlas in the AWS Marketplace How to identify and prevent fraudulent activities or transactions with a modern data architecture Data analytics and machine learning techniques to identify patterns and anomalies How to monitor transactions in real-time, analyse historical data, and flag any suspicious activity with with MongoDB Atlas and SageMaker Canvas

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July 3, 2024

MongoDB & Amazon Web Services (AWS) Fraud Detection Immersion Day (APAC timezone)

9:00am-11:30am IST | 11:30am-2:00pm SGT | 1:30pm-4:00pm AEST Not the right time zone? We are also running this on 19th June in EMEA and 24th July in AMER . Existing fraud detection systems often grapple with a critical limitation – relying on stale data – which limits the ability to access data in real-time to prevent fraudulent activities before they happen. Join MongoDB and AWS for a deep dive virtual workshop into Fraud Detection using MongoDB Atlas and Amazon SageMaker Canvas to learn how you can proactively identify and combat fraudulent activities, safeguarding systems and protecting customers. By leveraging MongoDB Atlas as an operational data store you are able to accommodate high-volume transactional data with exceptional performance and flexibility. Coupled with Amazon SageMaker Canvas, leverage AI/ML solutions effortlessly with a no-code platform that brings the power of advanced analytics for fraud detection to your fingertips. In the virtual workshop, you will learn: How to architect, design, build, and deploy applications with MongoDB Atlas in the AWS Marketplace How to identify and prevent fraudulent activities or transactions with a modern data architecture Data analytics and machine learning techniques to identify patterns and anomalies How to monitor transactions in real-time, analyse historical data, and flag any suspicious activity with with MongoDB Atlas and Sagemaker Canvas You will hear from: Bhavik Bhatt: Associate Solutions Architect at MongoDB Igor Alekseev: Data & Analytics Partner Solutions Architect at AWS

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

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. 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. 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 Finally, using MongoDB's aggregation framework to generate a complete picture of fleet management operations

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Enhancing Retail Operations with AI and Vector Search: The Business Case for Adoption

AI, or artificial intelligence, is quickly becoming a universal tool that fits in every retailer’s toolbox. Today, generative AI-enriched applications go beyond text data, creating hyper-personalized experiences with capabilities like advanced semantic search or user-prompted conversations. While implementing AI technology can be risky, complex, and time consuming, the potential for benefits like higher profits, faster innovation, and lower costs are driving industries toward an AI-powered future. The world’s most innovative retailers haven’t missed a beat in turning robust operational data into the ingredients for powerful applications with the potential to transform the status quo. In this paper, we will explore the top use cases across the retail industry that are infused with MongoDB Atlas AI capabilities.

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Atlas Vector Search Webinar (English)

Watch this exclusive webinar, Atlas Vector Search, where we're demonstrating a few key search capabilities: During this time, you will learn how to: How to use MongoDB Atlas for artificial intelligence-based applications Use cases of Atlas Vector Search Integration of MongoDB Atlas with LLM

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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, joined Fern Halper, VP and Sr. Research Director for Advanced Analytics at TDWI for an informal chat about building and deploying generative AI applications. Topics include: 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

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Build smart applications with MongoDB Atlas and Google Cloud Vertex AI

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. In this webinar you will learn more about the integration of MongoDB Atlas Vector Search with Google Cloud Vertex AI. On the agenda: MongoDB & Google Cloud: Powering the next generation of GenAI applications Deep dive into the unified ML Platform and flexible data model Live demo

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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? Stephen O’Grady, Principal Analyst & Co-founder at RedMonk joined Benjamin Flast, Product Management Lead at MongoDB for a discussion on these tectonic trends, including: 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.

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How Leading Industries are Transforming with AI and MongoDB Atlas

AI, or artificial intelligence, is quickly becoming a universal tool that fits in every industry’s toolbox. While implementing AI technology can be risky, complex, and time-consuming, the potential benefits, such as higher profits, faster innovation, and lower costs, are driving industries toward an AI-powered future. The world’s most innovative industries haven’t missed a beat in turning robust operational data into the ingredients for powerful applications with the potential to transform the status quo. In this paper, we will explore the top use cases across the six core industries that are infused with MongoDB Atlas AI capabilities

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MongoDB Evolves Its Portfolio with AI at the Top

Businesses will tap the power of AI by pairing their own business data with generative AI and large language models (LLMs) to deliver the next wave of intelligent, business-specific, applications. But to achieve that, we’ve got to free developers from the tedious and laborious tasks that dominate their workload today. That’s a key observation of IDC’s Market Perspective. The report takes an in-depth look at how MongoDB integrates GenAI into its developer data platform to empower developers with advanced AI capabilities and bring them a powerful integration with Amazon CodeWhisperer. Read the full report to learn: How MongoDB uses Atlas Vector Search to integrate corporate ops data and automate complex coding tasks for smart apps How MongoDB’s partnership with Amazon accelerates coding of GenAI apps How MongoDB speeds legacy database migrations How MongoDB's early GenAI capabilities position it in the innovator space while making its offerings richer and more attractive for developers

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