Introducing the Next Generation of MongoDB Education

Cara Heimbaugh
November 15, 2022
#University

MongoDB University has always offered developers free, self-paced, on-demand ways to learn MongoDB and advance their careers. Now MongoDB has launched an enhanced University experience, with a rollout of new courses and features, and a seamless path to MongoDB certification to help take your skills and career to the next level.

“MongoDB has always been a developer-first company. But it’s one thing to say that and support the current generation of developers and MongoDB users, it’s another to play a larger role in molding the developers of the future,” says Mark Porter, Chief Technology Officer, MongoDB. “Developers have gone from being a curiosity when I began my career to becoming a boardroom priority. If software and applications are the currency of the modern day economy, development teams are the market makers, and we want to support them on this journey.”

Announced at our annual .local London developer conference, the new learning experience makes it easy to quickly pick up knowledge, develop a fundamental MongoDB skillset, and get certified. Beginning November 15, you can discover:

New courses that make it easy to learn how to use MongoDB in the context of your preferred programming language, including Python, C#, Java, and Node.JS
New discounts and incentives to support your growth. Now, any time you complete a new developer learning path, you will receive a 50% discount on the Associate Developer certification exam fee. And as always, all course content is free.
Easier accessibility to course videos - registration is no longer required! Those who do register gain access to hands-on labs, quizzes, and certifications, as well as the ability to track their progress.
Language Subtitles for all new courses. Chinese (Traditional and Simplified), Korean, Spanish, French, and Portuguese subtitles are now available.
Short form content on newer features. Try our new “Learning Bytes” to build knowledge on MongoDB in 20 minutes or less.
An enhanced certification experience with 24/7 exam access and robust study materials, including videos, study guides, and practice questions. Additionally, MongoDB University now offers course certificates and digital certification badges through Credly that strengthen your professional profiles on LinkedIn, and can be shared across Twitter and Facebook. Adding these badges will enhance your opportunities for roles that require MongoDB experience and enter you into the Credly talent pool, making you visible to recruiters and hiring managers looking for specific certifications.
Updated labs that provide guided, hands-on activities that allow you to practice what you’ve learned and see real-time results of your work.

Getting started with MongoDB University

Badges

Previous certifications continue to be valid and will now include digital badges, so be on the lookout for an email from our badging partner, Credly. You can then accept your MongoDB Certified badge and share it on LinkedIn, Twitter, and Facebook. When you accept, you will also be included in the Credly Talent Directory, which enables recruiters, hiring managers, and others to connect with you about opportunities based on the specific digital credentials you’ve earned.

In-progress learning

If you currently have in-progress courses with MongoDB University, those courses must be completed by December 1st in order to have your certificate transfer to the new University experience. If you don’t complete a course by the deadline, that course will still be available in the new University, but your progress will not be transferred.

Ready to explore all the new possibilities? Get started with the next generation of MongoDB University today.

Start learning today!

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Built by MongoDB: Qubitro Makes Device Data Accessible Anywhere it's Needed

Increased cloud adoption and the expansion of 5G networks are expected to drive growth in IoT technologies over the next few years. Emergent IoT technologies are poised to transform businesses and the social fabric, including healthcare, smart homes and cities, and the government sector. Delaware-based startup, Qubitro , looks to capitalize on the potentially explosive growth in IoT technology by helping companies bring smart solutions to market faster. Qubitro, which is also a member of the MongoDB for Startups program, offers the fastest way of collecting and processing device data to activate it wherever it's needed. Product vision Qubitro founder and CEO, Beray Bentesen, estimates that there are now billions of devices producing massive amounts of data. The company's mission, he says, is to make device data accessible anywhere it's needed as fast as possible and at a lower cost than ever before. By collecting device data from multiple networks and providing various developer toolkits for activating data in applications, Qubitro enables data-driven decision making and modern application development. The company has two main products: the Qubitro Portal , a user interface where users can collaborate with other members or their internal team and create real-time actions such as rules and output integrations with their applications, and developer tools including APIs and SDKs that allow for custom solutions without having to develop data infrastructure from scratch. Bentesen wants Qubitro to become the fabric of a digital transformation powered by device data. "We aim to make any data published from devices flow over our network and make any application that relies on device data to integrate with our services," Bentesen says. The ideal Qubitro customer is one that needs to put device data into their solutions. "It could be startups, IoT-adopting enterprises, or custom solution providers," Bentesen says. The company has also been heavily investing in developer experience, he adds. A platform to build upon The secret to building a platform that can process data in milliseconds with privacy and user experience combined is, not surprisingly, another platform — specifically the MongoDB Atlas developer data platform. "We offer managed connectivity solutions, user interface, and the APIs," Bentesen says. "So we process tons of data. And MongoDB is in the middle of all those inputs and outputs." The MongoDB for Startups program helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Bentesen says the company has benefitted from being in the program a number of ways. "In the early days when we joined the program, we were able to get answers to questions that would take probably weeks or maybe more if you search on the internet," he says. "We were able to understand what to develop, which saved us a lot of time and of course expense." The MongoDB Atlas platform also helps their developers during those crucial stages prior to launching a new feature and as the product grows in popularity. "With MongoDB Atlas, we could test our development environment before going to production," Bentesen says. "And as we scale, we're able to observe the traffic through MongoDB Atlas and optimize thanks to the tools MongoDB offers, like MongoDB Compass , without dealing with code or complex environments." MongoDB's document model database made it an easy choice for the company's needs. "We decided to use MongoDB because it's a flexible environment," Bentesen says. "We knew we would have to build new features over time. So we needed to go with a flexible database. We're still adding more and more features without breaking the entire system. We wanted that flexibility, and in a managed cloud offering, which MongoDB gives us." Bentesen also cites MongoDB's Time Series collections as one of the features he's most excited about, since the vast majority of IoT solutions rely on time series data. Looking forward Bentesen says Qubitro will likely add more enterprise features in the future. The more they grow, he says, the more insight they're getting about what customers want. The company also plans to invest heavily in growing its community of users and, of course, attracting more talent. Bentensen says the company fully embraces the remote-first culture and believes they can work faster working remotely. If you're looking forward to building the next generation of connected solutions, visit Qubitro.com , join the company's Discord server , or have a chat anytime, even weekends! Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now . For more startups content, check out our previous blog on ChargeHub .

November 9, 2022

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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