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

Gregory Maxson
January 18, 2024

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.

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Navigating the Landscape of Artificial Intelligence: How Can The Financial Sector Make Use of Generative AI

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . In the ever-evolving landscape of financial technology, the conversation around artificial intelligence (AI), and particularly generative AI is gaining momentum. AI has been part of the financial landscape for decades but with the advances in generative AI come greater benefits but also risks that financial institutions need to consider in such a regulated industry. While the potential benefits of generative AI are significant and the adoption by many is still being considered, a measured approach is needed when moving from proof of concept into production. In an edition of the Fintech Finance News Virtual Arena , several notable industry thought leaders from HSBC, Capgemini, and MongoDB came together to explore how the financial sector can make use of generative AI and what financial institutions must consider in their AI strategy. Watch the panel discussion How can the financial sector make use of generative AI today with HSBC, MongoDB and Capgemini . Hear from: EJ Achtner, Office of Applied Artificial Intelligence at HSBC Dan Pears, Vice President, UK Practice Lead at Capgemini Wei You Pan, Director, Financial Services Industry Solutions at MongoDB Doug Mackenzie, Chief Content Officer at FF News Addressing the challenges of generative AI While financial technologists have always had to deal with persistent issues like risk management and governance, the adoption of generative AI in fintech introduces new challenges that AI specialists have always dealt with, like inherent biases and ethical concerns. One challenge that stands out for generative AI is hallucination — the generation of content that is not accurate, factual, or reflective of the real world. AI models may produce information that sounds plausible but is entirely fictional. Generative AI models, especially in natural language processing, might generate text that is coherent and contextually appropriate but lacks factual accuracy. This poses challenges in different domains, including misinformation and content reliability. Examples of such challenges or risks may include: Misleading financial planning advice: In financial advisory services, hallucinated information may result in misleading advice leading to unexpected risks or missed opportunities. Incorrect risk assessments for lending: Inaccurate risk profiles may lead to poor risk assessments for loan applicants that can cause a financial institution to approve a loan at a higher risk of default than the firm would normally accept. Sensitive information in generated text: When generating text, models may inadvertently include sensitive information from the training data. Adversaries can craft input prompts to coax the model into generating outputs that expose confidential details present in the training corpus. It is thus paramount that financial institutions understand the technological impact, scale, and complexity associated with AI, especially the generative AI strategy. A strategic and comprehensive approach that encompasses various aspects of technology, data, ethics, and organizational readiness is critical. Here are some key considerations financial institutions must consider when adopting such a strategy: Hallucination mitigation: Mitigating hallucination in generative AI is a challenging task, but several strategies and techniques can be employed to reduce the risk of generating inaccurate or misleading information. One promising strategy is to make use of the Retrieval Augmented Generation (RAG) approach to mitigate hallucination in generative AI models. This approach involves incorporating information retrieval mechanisms to enhance the generation process, ensuring that generated content is grounded in real-world knowledge. Vector Search is a popular mechanism to support the implementation of the RAG architecture which uses vector search to retrieve relevant documents based on the input query. It then provides these retrieved documents as context to the large language models (LLM) to help generate a more informed and accurate response. Data quality and availability: Take a step back before adopting AI to ensure the quality, relevance, and accuracy of data being used for AI training and decision-making can be accessed in real time. Education: Investing in training programs is key to addressing the skills gap in AI, ensuring the workforce is equipped to manage, interpret, and collaborate with AI technologies. For the adoption of AI to be successful, a culture of learning and development is vital, providing employees with the tools needed to be the absolute best that they can be for their personal and professional development. Furthermore, promoting awareness about potential vulnerabilities and continuously refining models to enhance their resilience against hallucination, biases, adversarial manipulation, and other weaknesses are essential to ensure success in generative AI applications. Develop new governance, frameworks, and controls: Before going live, create safe and secure environments for testing and learning that allow you to fail fast in a safe manner. Moving headfirst into production with direct contact with customers can result in the wrong governance methods being implemented. Monitoring and continuous improvement: Implement robust monitoring systems to measure and understand financial impacts, change impacts, scale, and complexity associated with the adoption of AI. Scalability and integration: Design AI systems with scalability in mind to accommodate growing datasets and evolving requirements. Security and privacy: Implement robust cybersecurity measures to safeguard AI models and the data they rely on. Techniques such as adversarial training, input sanitization, and incorporating privacy-preserving mechanisms can help mitigate the risk of generative AI inadvertently revealing private data. Incident response plans should be part of the cybersecurity measures, as well as regular education of the relevant stakeholders on security and privacy. How MongoDB can help you overcome your data challenges In the realm of adopting advanced technologies like AI and ML which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems, particularly when it comes to use cases such as fraud prevention where the platform is integrated with external sources for accurate data analysis on complete data. The inflexibility of existing systems poses a significant pain point, hindering the seamless incorporation of cutting-edge technologies. MongoDB serving as the operational data store (ODS) with a flexible document model enables financial institutions to efficiently handle large volumes of data in real time. By integrating MongoDB with AI/ML platforms, businesses can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. Legacy systems, marked by their inflexibility and resistance to modification present another challenge in the pursuit of leveraging AI to enhance customer experiences and improve operational efficiency. Integration struggles also persist, especially in the financial sector, where the uncertainty of evolving AI models over time requires a scalable infrastructure. MongoDB's developer data platform future-proofs businesses with its flexible data schema capable of accommodating any data structure, format, or source. This flexibility facilitates seamless integration with different AI/ML platforms, allowing financial institutions to adapt to changes in the AI landscape without extensive modifications to the infrastructure. Concerns regarding the security of customer data, especially when shared with third parties through APIs, further complicate the adoption of innovative AI technologies. Legacy systems can stand in the way of innovation as they are often more vulnerable to security threats due to outdated security measures. MongoDB’s modern developer data platform addresses these challenges with built-in security controls across all data. Whether managed in a customer environment or through MongoDB Atlas , a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive financial data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to discover more about building AI-enriched applications with MongoDB, take a look at the following resources: Mitigate hallucination of Generative AI by using RAG with Atlas Vector Search, LangChain, and OpenAI Deliver AI-enriched apps with the right security controls in place, and at the scale and performance users expect Sign up for our Atlas for Industries programme to get access to our solution accelerators to drive innovation

January 18, 2024
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The Developers' Developers: Two Australian Developers Share Their Connections to Customers

The world’s 28 million software developers are writing the foundations of our future, propelling innovation for their organizations through lines of code by creating game-changing new apps. Indeed, the US Bureau of Labor Statistics predicts that between 2022 and 2032, the number of software developers, quality assurance analysts, and testers will grow 24%, “much faster than the average for all occupations.” Fueling this innovative workforce is another group of developers, the people working behind the scenes to build the tools, technologies and platforms that other developers need to be successful: the developers’ developers. Many developers at MongoDB—which after all was built by developers for developers, and is beloved by enterprises and startups alike—fall into this camp. To learn more about what makes these developers tick, we talked with two Australia-based senior software engineers at MongoDB who love to code for their peers. For Lavender Chan and Angus Lee, there’s nothing like seeing the ripple effect of the code they have been working on and the impact it has on their customers. What’s more, the opportunity to be a “developer’s developer” has allowed Chan and Lee to find a space for deep technical work while thriving in an autonomous environment. At MongoDB we believe developers will build the future. First, can you share more about your roles and what you’re working on? Lavender Chan (LC): I work on the Relational Migrator tool, which allows developers to migrate SQL data onto MongoDB. I joined the company two and a half years ago, and have been part of the Sydney technology scene for the last 10 years. The appeal of joining MongoDB was that it’s a large global company, but in the engineering team you are able to have a big impact and a lot of autonomy. Relational Migrator was a greenfield project, and our team has been able to take the original product idea built out of the US and run with it. I’m a full stack developer and have touched on every feature of the tool. A lot of the engineers were able to contribute and work on new ideas. There’s also a strong emphasis on culture here which practically means a lot of the people I work with are excited to be here and passionate about their roles. Angus Lee (AL): I work in the MongoDB Charts team in Australia and think our team is a sweet spot for developers. I’ve interned for other tech companies and started my career at MongoDB. Since then I’ve been given responsibilities where I can create a lot of impact. My role at MongoDB in Sydney has also given me great opportunities to connect directly to the developers we are creating products for in a way that pushes my work to a higher level. Your roles are focused on products targeted to other developers. How does developing for developers affect your approach to your work? LC: In our roles we are creating directly for other developers, so the work that I am doing is deeply technical and specific. As Migrator is a newer product, we are able to interact directly with our customers—other developers—and often a lot of their questions are quite complex and specific, which means I go on a learning journey in debunking and fixing their problems. AL: We have a strong team culture in that as developers we want to be our own users. That means we want to use other MongoDB team products, and they use ours, so we can better identify pain points and issues for our customers. There’s a term that developers use called “dogfooding” that really sums up how we think on this, which basically means to use your own product. It means for me that I think about writing clean code to help any other developers extend on this, and how effective what I do will be for the user. What I’ve also learnt is how our product helps other products thrive. We should have done all the hard work to transform data and show it through data visualizsation tools so it’s easy for the customer. Can you tell us more about this connection to customers and how MongoDB empowers developers? LC: When Relational Migrator was released as a general product, I went to MongoDB World to work at the booth, and I talked to the developers and customers using the platform. As an engineer, it was an amazing experience and opportunity to see how it was being used and what else we could be doing. This connection of engineers with customers, as well as the ability to speak to them regularly in my role, is unique. In other companies I would need to go through support teams, to go through someone else just to push out a bug fix. Our team is very customer focused, so we can prioritizse features that our customers want. AL: One of the best moments for me at MongoDB was when I went to MongoDB World and I sat down with a customer to talk through a feature of Charts. It was a pivotal moment to see the improvements it makes for the businesses that use it, and the impact it generates for their customers beyond that. I could sit back and see the ripple effect of the code I’m writing. There is also a great feedback engine where our users can submit ideas and other users can vote for that feature. The product managers pick from these and push out features that are directly relevant to the developers using it. Where we really connect is in our aim to create an open forum for developers and customers to provide feedback and suggest ideas. Developers are problem solvers. As part of the MongoDB Love Your Developers campaign, we believe in championing the voice of developers and giving them the freedom to experiment and innovate. How do you see this in action? LC: In other places I was a small cog in a massive system. At MongoDB, I really have an impact, and can see directly how my work translates to our final product. In Sydney, we’re a satellite office, but it’s indicative of our company culture that there is huge trust placed in these teams. We’re given high impact projects and can run with them, which means I’ve been able to watch the Relational Migrator product go from a tiny product used by only a few customers, to one that is now generally available. Not many engineers get to work for a well-established,large company and still have the opportunity to work on and release products like a startup. There is a strong global interest in AI-driven innovations. How have your connections to customers led to innovations in this area? AL: We’ve been able to take an idea for a new AI feature, Natural Language Charts , and take it from concept to being released as a feature at MongoDB.local in London. We could see from our conversations with customers, as well as broader industry trends, that there was strong interest in new AI features so we were able to prioritizse it for Charts. We started with nothing and were given the freedom to research how this feature could work using AI, create a new proof of concept, and from there we were able to push it out into a feature which was a really proud moment. Having this agility and flexibility to prioritize something new is exactly what we want to provide to our customers. I never feel like I'm just churning out code. We are connected to the work and to our customers. MongoDB is built by developers, for developers. Become part of the team changing the way the world works with data!

May 9, 2024