From field to product and developer relations, our Marketing organization is growing around the world 🌎 Where in the world was this EMEA Field Marketing team offsite? Drop your guesses in the comments 👇 and sign up for our talent community to learn more about careers at MongoDB 👉 https://lnkd.in/gD3Egz-V 📸 Laia Barnés, Field Marketing Specialist #LifeAtMongoDB
MongoDB
Software Development
New York, NY 768,499 followers
A developer data platform for developers to do their best work. #LoveYourDevelopers
About us
Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, our developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today's wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. To learn more, visit mongodb.com.
- Website
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http://www.mongodb.com
External link for MongoDB
- Industry
- Software Development
- Company size
- 5,001-10,000 employees
- Headquarters
- New York, NY
- Type
- Public Company
- Founded
- 2007
- Specialties
- open source, databases, mongodb, and software developer
Products
MongoDB
NoSQL Database Software
MongoDB’s developer data platform integrates all of the data services you need to build modern applications in a unified developer experience. It handles transactional workloads, app-driven analytics, full-text search, AI-enhanced experiences, stream data processing, and more, all while reducing data infrastructure sprawl and complexity.
Locations
Employees at MongoDB
Updates
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Imagine an AI-powered app that uses real-time sounds to diagnose future problems. This is what’s possible when you unify operational and vector data on one platform that puts developers first. Learn more here: https://lnkd.in/gyvd7v-m #LoveYourDevelopers
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In this livestream, we will deep-dive into evaluating LLM applications. We will talk about the evaluation process and metrics, along with a code walkthrough of how to evaluate a RAG application using the RAGAS framework. - ✅ Link to this article: https://lnkd.in/gA5db5DM ✅ Sign-up for a free cluster at → https://lnkd.in/g-_TF8cC ✅ Get help on our Community Forums → https://lnkd.in/geTWpQyC #MongoDBTV
Evaluating your RAG Applications
www.linkedin.com
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Learn how Jacob Latonis stays at the top of his game with MongoDB University! Explore over 1,000 free learning assets, programming-language-specific courses, hands-on labs, and short Learning Bytes. ➡️ https://lnkd.in/gaATnn7A
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As RAG and agent-based LLM applications hit production, keeping operational costs down is key. Our free course on Prompt Compression and Query Optimization, in partnership with DeepLearning.AI, will teach you how to combine traditional and vector database techniques to make RAG more cost-effective and efficient. In the course, you will learn to: ✂️ Reduce prompt length to save inference costs. 🗂️ Filter results based on conditions, applied at index creation or after vector search. 🔝 Reorder search results to improve relevance. 📋 Select a subset of fields to minimize inputs to LLM. Enroll today and start optimizing: https://lnkd.in/gD4_v5bh Andrew Ng, Richmond Alake
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LLMs are transforming the search game as we know it. Here’s how you can play: 🔍 Outdated keyword-based algorithms with their rigid data structures and irrelevant results are a thing of the past. 🔍 LLM-powered search models bring scalable and customizable solutions, delivering improved experiences for users seeking information. 🔍 Level up your search models with Retrieval Augmented Generation (RAG), and utilize your unique data to build a more intuitive, contextual search experience. Future-proofing your search strategy can be complex, but MongoDB makes it simple for developers who already have enough on their plate to focus on building. 🛠 Discover how MongoDB can streamline your RAG building process: https://lnkd.in/gVt3a9n7
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MongoDB is partnering with #AI industry leaders and emerging players to help developers and businesses quickly and safely build cutting-edge AI applications. In June, we welcomed seven new AI partners that offer product integrations with MongoDB: AppMap, Mendable, One AI, Prequel, Qarbine, Temporal Technologies, and unstructured.io. Learn more about these integrations and how they can help you drive AI innovation. https://lnkd.in/griXuxx3
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How do we make building AI apps easier and faster? (Hint: It starts with unifying operational and vector data) Learn more here: https://lnkd.in/g_cA3isg #LoveYourDevelopers
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Agentic systems are now a reality. 🤖 Understanding #AI agents and agentic systems isn't hard. In fact, there are familiar components if you've already built RAG systems and pipelines. In this latest tutorial we will walk you through: 1️⃣ Integrating agentic components with existing RAG architectures 2️⃣ Leveraging Anthropic's Claude 3.5 Sonnet's advanced reasoning for complex problem-solving 3️⃣ Using MongoDB as a robust memory solution for AI agents 4️⃣ Implementing agentic RAG using LlamaIndex Check out the full tutorial and drop your questions in the comments 👇 https://lnkd.in/g-5_yPvk Richmond Alake
How to Implement Agentic RAG Using Claude 3.5 Sonnet, LlamaIndex, and MongoDB | MongoDB
mongodb.com
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