MongoDB AI Course in Partnership with Andrew Ng and DeepLearning.AI

MongoDB is committed to empowering developers and meeting them where they are. With a thriving community of 7 million developers across 117 regions, MongoDB has become a cornerstone in the world of database technology.

Building on this foundation, we're excited to announce our collaboration with AI pioneer Andrew Ng and DeepLearning.AI, a leading educational technology company specializing in AI and machine learning. Together, we've created an informative course that bridges the gap between database technology and modern AI applications, further enhancing our mission to support developers in their journey to build innovative solutions.

Introducing "Prompt Compression and Query Optimization"

MongoDB’s latest course on DeepLearning.AI, Prompt Compression and Query Optimization, covers the prominent form factor of modern AI applications today: Retrieval Augmented Generation (RAG). This course showcases how MongoDB Atlas Vector Search capabilities enable developers to build sophisticated AI applications, leveraging MongoDB as an operational and vector database.

To ensure that learners taking this course are not just introduced to vector search, the course presents an approach to reducing the operational cost of running AI applications in production by a technique known as prompt compression.

“RAG, or retrieval augmented generation, has moved from being an interesting new idea a few months ago to becoming a mainstream large-scale application.” — Andrew Ng, DeepLearning.AI

Key course highlights

  1. RAG Applications: Learn to build and optimize the most prominent form of AI applications using MongoDB Atlas and the MongoDB Query Language(MQL).

  2. MongoDB Atlas Vector Search: Leverage the power of vector search for efficient information retrieval.

  3. MongoDB Document Model: Explore MongoDB's flexible, JSON-like document model, which represents complex data structures and is ideal for storing and querying diverse AI-related data.

  4. Prompt Compression: Use techniques to reduce the operational costs of AI applications in production environments.

In this course, you'll learn techniques to enhance your RAG applications' efficiency, search relevance, and cost-effectiveness. As AI applications become more sophisticated, efficient data retrieval and processing becomes crucial. This course bridges the gap between traditional database operations and modern vector search capabilities, enabling you to confidently build robust, scalable AI applications that can handle real-world challenges.

MongoDB's document model: The perfect fit for AI

A key aspect of this course is that it introduces learners to MongoDB's document model and its numerous benefits for AI applications:

  • Python-Compatible Structure: MongoDB's BSON format aligns seamlessly with Python dictionaries, enabling effortless data representation and manipulation.

  • Schema Flexibility: Adapt to varied data structures without predefined schemas, matching the dynamic nature of AI applications.

  • Nested Data Structures: Easily represent complex, hierarchical data often found in AI models and datasets.

  • Efficient Data Ingestion: Directly ingest data without complex transformations, speeding up the data preparation process.

Leveraging the combined insights from MongoDB and DeepLearning.AI, this course offers a perfect blend of practical database knowledge and advanced AI concepts.

Who should enroll?

This course is ideal for developers who:

  • Are familiar with vector search concepts

  • Building RAG applications and Agentic Systems

  • Have a basic understanding of Python and MongoDB and are curious about AI

  • Want to optimize their RAG applications for better performance and cost-efficiency

This course offers an opportunity to grasp techniques in AI application development. You'll gain the skills to build more efficient, powerful, cost-effective RAG applications, from advanced query optimization to innovative prompt compression.

With hands-on code, detailed walkthroughs, and real-world applications, you'll be equipped to tackle complex AI challenges using MongoDB's robust features. Take advantage of this chance to stay ahead in the rapidly evolving field of AI. Whether you're a seasoned developer or just starting your AI journey, this course will provide invaluable insights and practical skills to enhance your capabilities.

Improve your AI application development skills with MongoDB's practical course. Learn to build efficient RAG applications using vector search and prompt compression. Enroll now and enhance your developer toolkit.

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Building Gen AI with MongoDB & AI Partners | July 2024

My colleague Richmond Alake recently published an article about the evolution of the AI stack that breaks down the “comprehensive collection of integrated tools, solutions, and components designed to streamline the development and management of AI applications.” It’s a good read, and Richmond—who’s an AI/ML expert and developer advocate—explains clearly how the modern AI stack evolved from a set of disparate tools to the (beautifully) interdependent ecosystem on which AI development relies today. “The modern AI stack represents an evolution from the fragmented tooling landscape of traditional machine learning to a more cohesive and specialized ecosystem optimized for the era of LLMs and gen AI,” Richmond writes. In other words, this cohesive ecosystem is aimed at ensuring end-to-end interoperability and seamless developer experiences, both of which are of utmost importance when it comes to AI innovation (and software innovation overall). Empowering developer innovation is exactly what MongoDB is all about—from streamlining how developers build modern applications, to the blog post you’re reading now, to the news that the MongoDB AI Applications Program (MAAP) is now generally available. In particular, the MAAP ecosystem represents leaders from every part of the AI stack who will provide customer service and support, and who will work with them to ensure smooth integrations—with the ultimate aim of helping them build gen AI applications with confidence. As the saying goes, it takes a village. Welcoming new AI partners Because the AI ecosystem is constantly evolving, we're always working to ensure that customers can seamlessly integrate with the latest cohort of industry-leading companies. In July we welcomed nine new AI partners that offer product integrations with MongoDB. Read on to learn more about each great new partner! Enkrypt AI Enkrypt AI secures enterprises against generative AI risks with its comprehensive security platform that detects threats, removes vulnerabilities, and monitors performance for continuous insights. The solution enables organizations to accelerate AI adoption while managing risk and minimizing brand damage. Sahil Agarwal, CEO of Enkrypt AI said, “We are thrilled to announce our strategic partnership with MongoDB, to help companies secure their RAG workflows for faster production deployment. Together, Enkrypt AI and MongoDB are dedicated to delivering unparalleled safety and performance, ensuring that companies can leverage AI technologies with confidence and improved trust.” FriendliAI FriendliAI’s mission is to empower organizations to harness the full potential of their generative AI models with ease and cost efficiency. By eliminating the complexities of generative AI serving, FriendliAI aims to empower more companies to achieve innovation with generative AI. “We’re excited to partner with MongoDB to empower companies in testing and optimizing their RAG features for faster production deployment,” said Byung-Gon Chon, CEO and co-founder of FriendliAI. “MongoDB simplifies the launch of a scalable vector database with operational data. Our collaboration streamlines the entire RAG development lifecycle, accelerating time to market and enabling companies to deliver real value to their customers more swiftly.” HoneyHive HoneyHive helps organizations continuously debug, evaluate, and monitor AI applications, and ship new AI features faster and with confidence. "We’re thrilled to announce our partnership with MongoDB, which addresses a critical challenge in GenAI deployment—the gap between prototyping and production-ready RAG systems,” said Mohak Sharma, CEO of HoneyHive. “By integrating HoneyHive's evaluation and monitoring capabilities with MongoDB's robust vector database, we're enabling developers to build, test, and deploy RAG applications with greater confidence. This collaboration provides the necessary tools for continuous quality assurance, from development through to production. For companies aiming to leverage gen AI responsibly and at scale, our combined solution offers a pragmatic path to faster, more reliable deployment." Iguazio The Iguazio AI platform operationalizes and de-risks ML & gen AI applications at scale so organizations can implement AI effectively and responsibly in live business environments. “We're delighted to expand our partnership with MongoDB into the gen AI domain, jointly helping enterprises build, deploy and manage gen AI applications in live business environments with our gen AI Factory,” said Asaf Somekh, co-founder and CEO of Iguazio (acquired by McKinsey). “Together, we mitigate the challenges of scaling gen AI and minimizing risk with built-in guardrails. Our seamlessly integrated technologies enable enterprises to realize the potential of gen AI and turn their AI strategy into real business impact." Netlify Netlify is the essential platform for the delivery of exceptional and dynamic web experiences, without limitations. The Netlify Composable Web Platform simplifies content orchestration, streamlines and unifies developer workflow, and enables website speed and agility for enterprise teams. "Netlify is excited to join forces with MongoDB to help companies test and optimize their RAG features for faster production deployment,” said Dana Lawson, Chief Technical Officer at Netlify. “MongoDB has made it easy to launch a scalable vector database with operational data, while Netlify enhances the deployment process and speed to production. Our collaboration streamlines the development lifecycle of RAG applications, decreasing time to market and helping companies deliver real value to customers faster." Render Render helps software teams ship products fast and at any scale. The company hosts applications for customers that range from solopreneurs, small agencies, and early stage startups, to mature, scaling businesses with services deployed around the world, all with a relentless commitment to reliability and uptime. Jess Lin, Developer Advocate at Render, said, “We’re thrilled to join forces with MongoDB to help companies effortlessly deploy and scale their applications—from their first user to their billionth. Render and MongoDB Atlas both empower engineers to focus on developing their products, not their infrastructure. Together, we're streamlining how engineers build full-stack apps, which notably include new AI applications that use RAG.” Superlinked Superlinked is a compute framework that helps MongoDB Atlas Vector Search work at the level of documents, rather than individual properties, enabling MongoDB customers to build high-quality RAG, Search, and Recommender systems with ease. “We're thrilled to join forces with MongoDB to help companies build vector search solutions for complex datasets,” said Daniel Svonava, CEO of Superlinked. “MongoDB makes it simple to manage operational data and a scalable vector index in one place. Our collaboration brings the operational data into the vector embeddings themselves, making the joint system able to answer multi-faceted queries like “largest clients with exposure to manufacturing risk” and operate the full vector search development cycle, speeding up time to market and helping companies get real value to customers faster." Twelve Labs Twelve Labs builds AI that perceives the world the way humans do. The company models the world by shipping next-generation multimodal foundation models that push the boundaries in video understanding. "We are excited to partner with MongoDB to enable developers and enterprises to build advanced multimodal video understanding applications,” said Jae Lee, CEO of Twelve Labs. “Developers can store Twelve Labs' state-of-the-art video embeddings in MongoDB Atlas Vector Search for efficient semantic video retrieval—which enables video recommendations, data curation, RAG workflows, and more. Our collaboration supports native video processing and ensures high-performance & low latency for large-scale video datasets." Upstage Upstage specializes in delivering above-human-grade performance AI solutions for enterprises, focusing on superior usability, customizability, and data privacy. “We are thrilled to partner with MongoDB to provide our enterprise customers with a powerful full-stack LLM solution featuring RAG capabilities,” said Sung Kim, CEO and co-founder of Upstage. “By combining Upstage AI's Document AI, Solar LLM, and embedding models with the robust vector database MongoDB Atlas, developers can create a powerful end-to-end RAG application that's grounded with the enterprise's unstructured data. This application achieves a fast time to value with productivity gains while minimizing the risk of hallucination.” But wait, there's more! To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub , and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem.

August 7, 2024
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Boosting Customer Lifetime Value with Agmeta and MongoDB

Nobody likes calling customer service. The phone trees, the wait times, the janky music, and how often your issue just isn’t resolved can make the whole process one most people would rather avoid. For business owners, the customer contact center can also be a source of frustration, simultaneously creating customer churn and unhappiness, while also acting as a black hole of information as to why that churn occurred. It doesn’t have to be this way. What if instead, customer service centers offered valuable ways to increase the Customer Lifetime Value (CLTV) of customers, pipelines of upsell opportunities, and valuable sources of information? That’s the goal of Agmeta.AI , a startup dedicated to giving businesses actionable insights to fight churn, identify key customers primed for upsell, and improve customer service overall. Lost in translation “We started with a very simple thesis±people call into contact centers because they have a problem. That is a real make-or-break moment. The opportunity for churn is very high… or that customer can be a great target for upselling,” said Samir Agarwal, CEO and co-founder of Agmeta. “All of this data sits in a contact center, and businesses don't ever get to see it,” he added. According to Samir, even the businesses that think they are collecting useful information on customer service interactions are instead collecting incorrect or incomplete information. Or worse, they’re analyzing the information they do record incorrectly. Every business today talks about the importance of customer experience (CX), but the challenge businesses face is how they quantify that CX. Many contact centers substitute call sentiment for CX, or use keywords to determine canned responses. For example, imagine if a customer calls into a service center and they have what appears to be a positive conversation with an agent. They use words and phrases like “thank you,” and “yes, I understand,” and reply “no, I do not have anything else to ask” at the end of a call in which their complaint is not resolved. After putting the phone down, the customer goes on to cancel the service, or worse, initiate a chargeback request with their credit card provider. In some businesses the customer service agent may manually mark such a call as positive’ The agent, after all, ‘answered all the customers' concerns.’ As this example illustrates, the sentiment of a call should not be confused with the measure of customer experience. Another common way businesses try to gather feedback is by sending a post-call survey. However, a problem with this approach is that industry response rates for surveys are close to 3%. This implies that decisions get made on that small sample, and may not take into account the other 97% of the customers who didn’t respond to the survey. Survey results are also frequently skewed, as those most likely to respond are also the ones who were most unhappy with the contact center interaction and want their voices heard. The MongoDB advantage Using machine learning and generative AI, backed by MongoDB Atlas , Agmeta’s software understands not only the content of the call, but the context too. Taking our example above, Agmeta’s software would detect that the customer is unhappy, despite their polite and ‘positive’ sounding conversation with the agent, and flag the customer as a potential churn or chargeback candidate in need of immediate attention. “We will give you a CSAT (customer satisfaction) score and a reason for that CSAT score within seconds of the call ending±for 100% of the interactions,” said Samir. For Agmeta to work, Samir and his team had to have a database ready to accept all kinds of data, including voice recordings, unstructured text, and constantly evolving schema. “We didn’t have a fixed schema, we needed a database that was as flexible as Agmeta needed to be. I’ve known of MongoDB forever, so when I started to look at databases it seemed an obvious choice to me,” he said. The ability to quickly and easily work with vectorized data for gen AI was also crucial. “MongoDB provides vector search capabilities in an operational database. Rather than having to add a bolt on a vector database and figure out the ETL, MongoDB solved this issue for me in a single product. The way I look at it, if you do a good job on Vector search, then my life as an entrepreneur and software builder becomes much easier,” Samir said. After assessing database options and multiple LLMs, Samir and his team chose to pair MongoDB Atlas with Google Cloud, taking advantage of Gemini on Google’s generative AI platform. “With Atlas on Google Cloud, there are zero worries about database administration, maintenance, and availability. This frees us up to focus on creating business value,” Samir said. “Another benefit of using MongoDB is the flexibility to use the customer’s MongoDB setup which gives the customer the peace of mind from the perspective of security and privacy of their data.” Customer service first With the power of generative AI and MongoDB, Agmeta can deliver a CSAT score that measures the customers’ true takeaway from the call. The CSAT score is a multi-dimensional score that takes into account areas including resolution (as the customer sees it), politeness, the onus on the customer, and many other attributes. In the short term, the primary use for this technology is to detect and flag those customers at risk of churn, filing a charge dispute with their card provider, or potentially upselling, giving businesses an opportunity to “see” what they could never find out before. “When we talk to customers, the number one thing they are concerned about is customer churn. Right now they operate completely blind with no idea why people are leaving them,” said Samir. “One large telecoms customer Agmeta is in talks with had no idea where their churn was happening. But when we described being able to assign every customer a CSAT score, they were very excited,” he added. And it’s not just about preventing churn. Businesses can identify happy customers too, targeting them for upsell opportunities. “One of the things we do is spot patterns of unanswered questions from product support interactions,” Samir added. “When we see ‘Oh look, suddenly there are a lot more calls because of a release,’ then we can flag this to product teams as a must-fix issue.” The future of customer service Agmeta aims to amalgamate customer information with current and past experiences to provide businesses a more holistic±and nuanced—picture of their customers, and more precise next steps they can take. “What we want to do is look back in time and see what else happened with this customer,” Samir said. “The goal is to provide businesses with targeted directives to minimize churn and grow customer lifetime value.” Retrieval-augmented generation plays a key role in Agmeta’s vision. This also means an expanded role for both MongoDB’s vector database as the source of information against which semantic searches can be run, as well as Gemini for both analysis and presentation of the directives for the business. You can learn more about how innovators across the world are using MongoDB by reviewing our Building AI case studies . If your team is building AI apps, sign up for the AI Innovators Program . Successful companies get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem. Additionally, if your company is interested in being featured in a story like this, we'd love to hear from you! Reach out to us at ai_adopters@mongodb.com .

September 10, 2024