10,000 SI Certifications and Counting: MongoDB and System Integrator Partners Reach Major Milestone Supporting GenAI Initiatives

Mike Moss
January 18, 2024

Today, tens of thousands of customers trust MongoDB with their businesses’ most critical workloads, while MongoDB’s system integrator (SI) partner technologies and services play a critical role in their ability to innovate. Customers rely on global and regional SIs to help get the most out of MongoDB and quickly build sophisticated applications.

In late 2022, as VP of global system integrator and consulting partners at MongoDB, I led the team that launched our System Integrators Certification Program to ensure our partners were equipped to help customers get the most out of MongoDB. The System Integrators Certification Program, designed around two core certifications, gives MongoDB’s SI partners both an introduction to MongoDB and a technical deep-dive that qualifies participants to architect MongoDB for modern, AI-enriched applications.

Since its inception, this program has quickly gathered steam, and, today, we are proud to say that we’ve issued over 10,000 certifications to associates and architects across MongoDB’s various system integrator partners.

Achieving 10K certifications is a big step, and I am truly thrilled that over 2,500 Capgemini consultants have participated in this program and achieved MongoDB SI certification over the last ten months,” said Eric Reich, global AI and data engineering head at Capgemini. “SI Certifications are helping our consultants keep abreast of the latest architectures on cloud and data modernization. More specifically, these certifications help MongoDB and Capgemini engage in joint engineering collaboration, building accelerators to support our customers in optimizing business outcomes from data- and AI-powered applications leveraging MongoDB.

The certification program is an exclusive enablement offering for our SI partners and can be completed on-demand or via instructor-led training. The rapid success and popularity of this certification program reinforces the value of working with MongoDB for our SI partners, the commitment of these partners to truly understand our product, and the desire of joint customers to do more with MongoDB.

MongoDB is a key partner for Accenture as its technology enables our clients in their modernization journey. MongoDB's importance to us continues to grow as more organizations are working with Accenture to become more efficient with their data platforms while leveraging their AI investments,” said Ram Ramalingam, senior managing director and global lead of platform engineering and intelligent edge at Accenture. “Skills building and certifications are a significant value to our partnership as this enables my team to provide expertise to our clients for their modernization needs.

In the near future, we plan to expand our partner certification offerings, including the addition of a new MongoDB Application Delivery Certification. This certification will include exclusive, partner-only, online learning and hands-on labs that teach application delivery fundamentals and implementation best practices. It will equip our SI partners with the ability to architect MongoDB and deliver at scale. The team and I can’t wait to see what our customers and system integrators build next.

To learn more about MongoDB’s partners — who help boost developer productivity with a range of proven technology integrations — visit the MongoDB Partner Ecosystem. Current SI partners can register for the MongoDB Certification Program.

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Collaboration for Breakfast: MongoDB and Partners Share AI Insights at AWS re:Invent

I’m old enough to remember when every tech conversation didn’t include the term “AI.” Hardly a day goes by without some mention of AI or generative AI (gen AI). But don’t just take my word for it: Google News search results for the phrase, “generative AI” have grown more than 2000% since ChatGPT was launched in November 2022. The AI excitement is more than just hype. For example, we’re seeing widespread adoption of AI across MongoDB’s tens of thousands of customers. Meanwhile, a recent GitHub survey showed 92% of developers have already incorporated gen AI into their work, and Gartner predicts that by 2027, 90% of new applications will incorporate machine learning models or services. MongoDB and our partners tapped into this excitement during AWS re:Invent. On November 29 — the same morning the company announced the integration of MongoDB Atlas Vector Search and Amazon Bedrock (and less than a week before Atlas Vector Search was made generally available ) — MongoDB held an AI-themed breakfast that reinforced the importance of partnerships during this transformative time. During the breakfast, MongoDB product leaders sat down with leaders from four of the company’s partners — Gradient, LangChain, Nomic, and Unstructured—to share insights about building the next generation of AI applications. Despite its 7 a.m. start time, the breakfast was packed with attendees from a range of industries and geographies — no small feat given re:Invent’s busy schedule — and excitement for AI was palpable. Given the broad interest in all things generative AI, organizations of all sizes want to learn how they can build the applications of tomorrow. This is where MongoDB and partners come in: MongoDB provides an integrated developer data platform that accelerates innovation by simplifying the application development process. To streamline AI innovation, MongoDB partners with organizations that offer complementary technology solutions, interoperability, flexibility, and reliability. Partnering to deliver a complete AI toolkit For example, Unstructured works with MongoDB to help organizations connect enterprise data stored in difficult formats like PDF and PNG to AI models. And the combination of MongoDB and LangChain's application framework makes it possible to build solutions that leverage proprietary company data. Meanwhile, with MongoDB Atlas Vector Search and Gradient , organizations can build, customize, and run private AI applications that leverage industry expert large language models (LLMs) to enhance performance. And last but hardly least, Nomic's tools allow users to visualize the unstructured data they store in MongoDB, to make AI more explainable and accessible. All told each partner’s offerings work with MongoDB products to create a comprehensive set of tools with which developers can build AI applications. At the breakfast, company leaders shared their thoughts on the current AI landscape, how their organizations collaborate with MongoDB, and what they see as the future of AI tools. “At AWS re:Invent, we showed how MongoDB is the best platform for building enterprise-ready generative AI apps,” said Andrew Davidson, senior vice president of product management at MongoDB. “Our powerful developer data platform — which works seamlessly with cutting-edge AI ecosystem partners to enable openly composable architecture and design — empowers developers to create compelling AI apps and experiences with greater interoperability, simplification, flexibility, and choice, pushing the boundaries of what's possible.” For example, LangChain Founding Software Engineer Jacob Lee noted that “it’s so, so early for generative AI. Most attendees at re:Invent had only just begun to consider principles and use cases for the technology. There is so much opportunity and potential impact yet to emerge that it will truly take the entire ecosystem's talents and creativity to explore it all.” “In short, the most important thing is to support each other and just keep building cool things,” said Lee. Brian Raymond, founder and CEO of Unstructured, agreed that it's very early for generative AI. "We should start seeing incremental, yet exciting, gains in the performance of multimodal foundation models as well as increased focus on smaller models that are cheaper to run at scale," Raymond added. "It's likely going to take more time to mature the emerging foundation model stack (marked by retrieval-augmented generation ) into a performant and cost-effective option for most organizations." Creating a seamless AI development experience Overall, the re:Invent breakfast conversation conversation highlighted how MongoDB and its partners are working together to create a holistic, seamless AI development experience. By working closely with partner organizations to augment its industry-leading solutions , MongoDB ensures enterprises have access to everything they need in one place to develop cutting-edge, modern AI applications that are scalable, secure, and enterprise-grade. “Gradient's mission is to democratize AI by making it more accessible to enterprises and developers,” said Chris Chang, CEO and co-founder of Gradient. “However in AI, data itself can be challenging which is why our partnership with MongoDB will allow users to make the most out of their data and leverage a best-of-breed technology to help power new AI features.” To learn more about MongoDB’s artificial intelligence solutions—including resources to build next-generation applications — visit MongoDB for Artificial Intelligence . If your organization wants to build the next big thing in AI with MongoDB, consider applying for the MongoDB AI Innovators Program .

January 18, 2024
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024