A Year of Thrill: Celebrating the New MongoDB University

Thalita Carrico
November 28, 2023 | Updated: November 29, 2023
#University

Staying ahead in the ever-evolving tech world is like being on a rollercoaster - it’s exciting but it can also make your head spin! When we set out to revamp MongoDB University, we wanted to provide developers with frictionless access to the learning content they needed to conquer their challenges. It’s been one year since the launch and we are over the moon about how far the new MongoDB University has come - a one-stop hub with fresh certifications and new content, all available online.

But none of this would have been possible without the incredible support of our engaged learners who have embarked on this ride with us. Our commitment to delivering top-notch educational resources has been nothing short of award-winning, earning us the prestigious Silver Excellence Award from the Brandon Hall Group in the category of ‘Best Advance in Creating an Extended Enterprise Learning Program’. The success of the new University has also been featured at industry events, including Cognition and the Customer Education Management Association Conference.

So, let’s buckle up and take a tour through the revamped MongoDB University!

New content

With over 1,000 learning assets, including videos, hands-on labs, code recaps, quizzes, and courses, there’s something for everyone. Plus, now we have you covered with language subtitles in Chinese (Traditional and Simplified), Korean, Spanish, German, Japanese, Italian, French, and Portuguese. The best part? All of the content is free, online, and you can take your time and learn at your own pace. Let’s explore three of our newest courses:

  • Data Modeling for MongoDB: This course guides you through the foundational steps of creating an effective data model in MongoDB, including identifying entities and workloads, mapping and modeling relationships between entities, and using key schema design patterns.

  • Atlas Essentials: In this course, you’ll gain the foundational knowledge and skills needed to use MongoDB Atlas, the multi-cloud developer data platform.

  • MongoDB for SQL Professionals: This course will help you leverage your SQL skills to get started with MongoDB quickly.

You can practice what you learn and gain valuable real-world skills with labs hosted in our in-browser development environment. The new experience allows you to explore hands-on exercises as part of our courses, or you can dive directly into a standalone lab. The labs include step-by-step instructions that guide you through each scenario and even provide hints along the way.

And for those looking for nuggets of MongoDB wisdom, explore the catalog of over 30 Learning Bytes. These short videos cover a wide variety of topics and are designed to help you get the knowledge you need quickly.

New certifications

Our freshly revamped certifications are recognized by professional institutions and are your ticket to having your knowledge and skills formally validated and recognized by MongoDB. They are a great way to elevate yourself in your current role and increase your marketability for future roles. Certifications come with bragging rights, inclusion in the Credly Talent Directory, and a shiny Credly badge that makes it easy for you to share your achievement.

So, let’s explore the two new certifications:

  • MongoDB Associate Developer: Certify that you possess the essential skills to create beginner-level applications utilizing MongoDB as a backing database for Java, Python, C#, PHP, or Java applications.

  • MongoDB Associate Database Administrator: Validate your MongoDB database administration skills by certifying your knowledge of building, supporting, and securing MongoDB infrastructure.

And if you need a boost, once you complete one of the certification learning paths you will automatically unlock a 50% discount on a certification exam. Educators and students can check out the Academia program to learn how to receive a free exam.

All aboard! This is just the beginning of the adventure and we are excited for what is yet to come. So, fasten your seatbelt, and let’s keep learning together!

With over 1,000 learning assets, MongoDB University has what you need to pick up new skills and advance your career. Explore free courses, practice with hands-on labs, and earn MongoDB certifications.

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深入理解用户需求，利用 AI 和 MongoDB 提升内容个性化

内容无处不在。 无论消费者寻找什么，也无论消费者所处任何行业，找到内容并不困难，关键在于如何找到对应的内容。而这就是 Concured 专注的领域。 Concured 是一家来自蒙特利尔的 AI 初创企业，致力于协助市场营销团队对标受众，有的放矢地打造网站和销售内容；同时，帮助内容营销团队脱颖而出，加速基于洞察驱动的内容个性化。 2015 年，首席执行官 Tom Salvat 创立 Concured，旨在助力内容营销机构更深入地理解受众需求，交付更具影响力的内容。 Built with MongoDB 栏目采访了公司成立约一年后加入公司的 首席技术官 Tom Wilson ，话题围绕 Concured 的人工智能使用情况、Wilson 加入 Concured 的过程以及公司未来的发展。 Built with MongoDB： Concured 是做什么的？ Tom Wilson： Concured 是一家软件公司，以过去 5-10 年间发展的人工智能技术为基础，帮助市场营销机构了解如何撰写具体领域的宣发物料，发掘自身内容亮点，把握竞争对手以及行业宣发现状。进而打造个性化的客户网站使用体验，最大化内容投资收益比。 Concured 已成功推出一套内容推荐系统，能够为每位访问者提供针对性服务。这套系统注重用户隐私，不使用任何第三方 cookie 或用户监视技术，完全基于网站用户的操作，通过访问者的点击行为勾勒出其兴趣领域。随着用户的兴趣和目的逐渐清晰，这套系统会尝试推荐新的阅读内容，比如博文、产品介绍或其他类型的内容。 Built with MongoDB：您刚刚提到了人工智能，那么 Concured 是如何使用人工智能的呢？ Wilson： 我们运用人工智能的场景有很多。有别于其他个性化系统，Concured 的卖点之一是不需要过长的整合期，也无需在日常管理中进行维护。实现的途径是借助 AI 机器人剖析客户网站的内容，发掘相关性，提取文本、标题和其他所有相关元数据，然后完成自动索引。 我们的系统利用先进的自然语言处理 (NLP) 技术，为每个文档生成语义元数据，在多维空间中与特定点相对应。另一方面是理解其中的关键实体，以及同一个网站中某一篇特定文章与其他文章之间的关系。我们利用 AI 支持的网络爬虫找到的所有内容都会自动附上海量元数据。 [图片] Built with MongoDB：AI 并不总是 100% 准确，您在 Concured 打造的 NLP 的准确度怎么样呢？ Wilson： 以内容推荐系统而言，很难断定什么是最佳推荐，因为即便是同一个人，根据日期或网络操作的不同，推荐也会有所变化。例如一些知名的推荐系统，如 Netflix、Amazon 和 Spotify，总是在猜我们接下来想看什么，但却没有一个是对应的答案。 正因如此，绩效评价变得非常困难。所以，我们采取的方式是不提供 100% 对应的答案，而是通过改变算法，来看访问者是否会点击更多的文章，是否会进入网站运营商定义的目标页面，比如产品页面或注册表单。在网站访问人员中，最终执行该操作的人数比例越高，说明推荐系统越出色。我们可以对比客户在启用 Concured 个性化系统前后的网站成功率。截至目前，我们已经看到 2-3 倍的提升，算法一直在完善中。 Built with MongoDB：您是何时加入到 Concured 团队的？ Wilson： 当时公司已经得到第一笔来自外部的巨额投资，条件之一是引入一位专业的首席技术官。这种情况在企业初创期比较常见，投资方想要介入企业架构，把控资金流向，减少鲁莽行事。所以，有些企业将其戏称为“家长式监督”。我不知道这算不算是我的角色。不过虽然当时团队已经很强大，但我还是从架构入手，从根上上确保我们能够实现后续的目标，以及更长期的战略规划和技术愿景。 Built with MongoDB：您的团队是如何选择 MongoDB 的？ Wilson： 我加入时，团队已经在使用 MongoDB。加入后的几个月里，我们讨论过是否要换用结构化的数据库，并且必须制定出一项决议。所以我才参与其中，经过深思熟虑，决定继续使用 MongoDB。事实证明是一项完全正确的决议，有利于我们实现最初的愿景。同时，我们将弃用 Google Cloud Platform 上的社区版本，换用 MongoDB Atlas Serverless。令人欣喜的是，因为新平台无服务器，我们将不再需要管理各种机器，还能够使用 Atlas 上的文本搜索功能，顺便简化一下我们的技术堆栈。作为一家企业，就我们当下所处的位置以及未来五年的发展方向而言，MongoDB 始终是我们正确的选择。 Built with MongoDB： Concured 的未来是什么样的？ Wilson： 就在我们交谈的过程中，未来已经被书写。此时此刻，越来越多和我们大客户有着相同需求的企业正在找到我们。这些企业那些有着海量、已存档的内容，需要继续从中盈利，继续发布更多内容。无论是否是大型企业，比如咨询或金融服务行业或传统的出版商，确定的一点是，以相应的 KPI 为基准，产出利益最大化的对应内容。 Built with MongoDB：您收到的最好反馈是什么？ Wilson： 我的团队给我的一条正面反馈，是说我有担当。如果他们遇到问题，我会出手解决或者减少阻力，这样他们就可以全力以赴解决问题。这是我的人生观：如果你用心领导团队，事情就会自然顺利推进。 对于任何投资内容宣发的企业而言，最大化投资回报都是无可争议的商业诉求。

November 28, 2023
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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