Welcome to the (Tech) Olympics!

Evan Alger
July 26, 2024

Welcome to the Tech Olympics, where code meets competition! With the 2024 Summer Olympics starting today, we thought it’d be fun to imagine developers as athletes, showcasing their skills in a series of thrilling events.

From relay races to coding challenges, the Tech Olympics would bring together the brightest minds in tech for a competition like no other. Whether you're a coding wizard, a bug-squashing maestro, or an AI aficionado, there would be something to test your limits and celebrate your talents.

Opening ceremony

The opening ceremony is one of the most iconic aspects of the Olympics. From the lighting of the torch to performances by local artists, the opening ceremony encapsulates the spectacle of the games, and is a necessity for the Tech Olympics.

The Tech Olympics opening ceremony would kick off with a grand procession of teams involved, adorned in attire representing their area of expertise. Next, there’d be a performance by artists and developers using augmented and virtual reality to blend art with cutting-edge technology. Finally, there would be the lighting of the torch, but instead of the flame being run across the country, an application would be written and passed between developers from around the world that, when run, would light the torch and start the games.

Now that we’ve kicked off the Tech Olympics, let's consider what its events might look like.

Code sprint relay

The "code sprint relay" would be a collaborative coding event where teams of developers would tackle a series of coding challenges in relay format.

The twist would be that each member could only code for a set period (say, 5-10 minutes) before handing the code off to the next person. This setup requires clear communication and strategic planning, as each coder must quickly understand and build upon their predecessor's work. Code sprint relay challenges would range from algorithm problems to debugging tasks, demanding various skills and swift adaptability.

This event would be fast-paced and dynamic, with a lively atmosphere filled with the buzz of coding and quick exchanges of ideas. Success would be measured not only by the completion of challenges but also by the efficiency and quality of the code, making this event a test of teamwork and technical skill under pressure.

Security capture the flag

Capture the flag might seem more like a kids’ game than an Olympic event, but trust us, there’d be nothing childish about this event.

The "security capture the flag" event would be an exciting cybersecurity competition in which participants would need to solve security-related challenges to capture hidden "flags." These challenges would range from web application exploits and reverse engineering, to cryptographic puzzles and network forensics. Working in teams, participants would race against the clock to uncover vulnerabilities, exploit them, and find the embedded flags within a controlled, simulated environment.

At the end, a debriefing session would highlight the most innovative solutions and techniques used. Success would be measured by the number of flags captured and the ingenuity of the approaches, showcasing participants' technical skills and strategic thinking under pressure.

Bug hunt

Have you ever built out your code and then, upon running it, realized that you made a mistake? If you have, you’ll understand just how intense this next event could be!

The "bug hunt challenge" would be a fast-paced competition in which participants are tasked with finding and fixing bugs within a complex codebase. Each individual would be given the same software project with numerous hidden bugs, ranging from simple syntax errors to intricate logical flaws. Participants must use their debugging skills and tools to identify and resolve as many issues as possible within a set time limit.

The event would be marked by intense focus and strategic problem-solving as competitors meticulously comb through the code. An automated system would verify the fixes instantly, ensuring accuracy and efficiency. Success would be measured by the quantity and severity of bugs resolved, along with the quality of the fixes, making this event a test of attention to detail and technical proficiency.

AI arena

We’d be remiss not to include an AI event! The "AI arena" event would be a competitive showcase where participants create machine learning models using a provided dataset to solve a specified problem. Teams would have several hours to analyze the data, create features, and train their models. The objective would be to develop a model with the highest accuracy and performance, balancing technical innovation with practical application.

In the end, teams would present their models to judges, explaining their methodologies and challenges faced. Judging criteria would include model accuracy, creativity, and clarity of the presentation, making this event a comprehensive test of technical and communication skills.

Location

Finally, you can’t have an Olympics without a city to host it. There are plenty of tech hubs to choose from—San Francisco, London, Beijing—but we thought it’d be more fun to pick a growing tech hub like Ha Noi, Vietnam, as our location.

Vietnam had the highest digital economy growth in Southeast Asia in 2022, putting it on the path to be named alongside other “tech giant” cities. Also, Vietnamese food is excellent!

During the games, local startups and tech companies would showcase their work on the world stage, and visiting developers would see the innovations that Vietnamese companies are working on.

Sadly, there won’t be an actual Tech Olympics this year, but maybe in the future, there will be. An event that will bring the world's best developers together to showcase their skills, foster friendly competition, and allow the world to see just how amazing developers are. If you have some ideas about other events you would want to see at a Tech Olympics, connect with us on X (Twitter) and let us know what your ideas are.

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Building Gen AI Applications Using Iguazio and MongoDB

AI can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, process automation, and more. According to McKinsey & Company, generative Al has the potential to deliver an additional $200-340B in value for the banking industry . One popular use case is customer service, where gen AI chatbots have quickly transformed the way customers interact with organizations. They handle customer inquiries and provide personalized recommendations while empathizing with them and offering nuanced support tailored to individual needs. Another less obvious use case is fraud detection and prevention. AI offers a transformative approach by interpreting regulations, supporting data cleansing, and enhancing the efficacy of surveillance systems. These systems can analyze transactions in real-time and flag suspicious activities more accurately, which helps institutions prevent monetary losses. In this post, we introduce the joint MongoDB and Iguazio gen AI solution which allows for the development and deployment of resilient and scalable gen AI applications. Before diving into how it works and its value for you, let’s first discuss the challenges enterprises face when operationalizing gen AI applications. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Challenges to operationalizing gen AI Building an AI application starts with a proof of concept. However, enterprises need to successfully operationalize and deploy models in production to derive business value and ensure the solution is resilient. Doing so comes with its own set of challenges such as: Engineering challenges - Deploying gen AI applications requires substantial engineering efforts from enterprises. They need to maintain technological consistency throughout the operational pipeline, set up sufficient infrastructure resources, and ensure the availability of a team equipped with a comprehensive ML and data skillset. Currently, AI development and deployment processes are slow, time-consuming, and fraught with friction. LLM risks - When deploying LLMs, enterprises need to reduce privacy risks and comply with ethical AI standards. This includes preventing hallucinations, ensuring unbiased outputs, filtering out offensive content, protecting intellectual property, and aligning with regulatory standards. Glue logic and standalone solutions - The AI landscape is vibrant, and new solutions are frequently being developed. Autonomously integrating these solutions can create overhead for ops and data professionals, resulting in duplicate efforts, brittle architectures, time-consuming processes, and a lack of consistency. Iguazio and MongoDB together: High-performing and simplified gen AI operationalization The joint Iguazio and MongoDB solution leverages the innovation of these two leading platforms. The integrated solution allows customers to streamline data processing and storage, ensuring gen AI apps reach production while eliminating risks, improving performance, and enhancing governance. MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational (structured and unstructured data), analytical, and AI data services into a single platform to streamline building AI-enriched applications . MongoDB’s flexible data model enables easy integration with different AI/ML platforms, allowing organizations to adapt to changes in the AI landscape without extensive infrastructure modifications. MongoDB meets the requirements of a modern AI and vector data store: Operational and unified: MongoDB’s ability to serve as the operational data store (ODS) enables financial institutions to efficiently handle large volumes of real-time operational data and unifies AI/vector data, ensuring AI/ML models use the most accurate information. It also enables organizations to meet compliance and regulatory requirements (e.g., 3DS2, ISO20022, PsD2) by the timely processing of large data volumes. Multi-modal: Alongside structured data, there's a growing need for semi-structured and unstructured data in gen AI applications. MongoDB's JSON-based multi-modal document model allows you to handle and process diverse data types, including documents, network/knowledge graphs, geospatial data, and time series data. Atlas Vector Search lets you search unstructured data. You can create vector embeddings with ML models and store and index them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases. Flexible: MongoDB’s flexible schema design enables development teams to make application adjustments to meet changing data requirements and redeploy application changes in an agile manner. Vector store: Alongside the operational data store, MongoDB serves as a vector store with vector indexing and search capabilities for performing semantic analysis. To help improve gen AI experiences with greater accuracy and mitigate hallucination risks, using a RAG architecture together with the multi-modal operational data typically required by AI applications. Deployment flexibility: MongoDB can be deployed self-managed on-premise, in the cloud, or in a SaaS environment. Or deployed across a hybrid cloud environment for institutions not ready to be entirely on the public cloud. Iguazio’s AI platform Iguazio (acquired by McKinsey) is an AI platform designed to streamline the development of ML and gen AI applications in production at scale. Iguazio’s gen AI-ready architecture includes capabilities for data management, model development, application deployment, and LiveOps. The platform—now part of QuantumBlack Horizon , McKinsey’s suite of AI development tools—addresses enterprises’ two biggest challenges when advancing from gen AI proofs of concept to live implementations within business environments. Scalability: Ensures uninterrupted service regardless of workload demands, scaling gen AI applications when required. Governance: Gen AI guardrails mitigate risk by directing essential monitoring, data privacy, and compliance activities. By automating and orchestrating AI, Iguazio accelerates time-to-market, lowers operating costs, enables enterprise-grade governance, and enhances business profitability. Iguazio’s platform includes LLM customization capabilities, GPU provisioning to improve utilization and reduce cost, and hybrid deployment options (including multi-cloud or on premises). This positions Iguazio to uniquely answer enterprise needs, even in highly regulated environments, either in a self-serve or managed services model (through QuantumBlack, McKinsey’s AI arm). Iguazio’s AI platform provides: Structured and unstructured data pipelines for processing, versioning, and loading documents. Automated flow of data prep, tuning, validating, and LLM optimization to specific data efficiently using elastic resources (CPUs, GPUs, etc.). Rapid deployment of scalable real-time serving and application pipelines that use LLMs (locally hosted or external) as well as the required data integration and business logic. Built-in monitoring for the LLM data, training, model, and resources, with automated model re-tuning and RLHF. Ready-made gen AI application recipes and components. An open solution with support for various frameworks and LLMs and flexible deployment options (any cloud, on-prem). Built-in guardrails to eliminate risks and improve accuracy and control. Examples: Building with Iguazio and MongoDB #1 Building a smart customer care agent The joint solution can be used to create smart customer care agents. The diagram below illustrates a production-ready gen AI agent application with its four main elements: Data pipeline for processing the raw data (eliminating risks, improving quality, encoding, etc.). Application pipelines for processing incoming requests (enriched with data from MongoDB’s multi-modal store), running the agent logic, and applying various guardrails and monitoring tasks. Development and CI/CD pipelines for fine-tuning and validating models, testing the application to detect accuracy risk challenges, and automatically deploying the application. A monitoring system collecting application and data telemetry to identify resource usage, application performance, risks, etc. The monitoring data can be used to improve the application performance further through an RLHF (reinforcement learning from human feedback) integration. #2 Building a hyper-personalized banking agent In this example, accompanied by a demo video , we show a banking agent based on a modular RAG architecture that helps customers choose the right credit card for them. The agent has access to a MongoDB Atlas data platform with a list of credit cards and a large array of customer details. When a customer chats with the agent, it chooses the best credit card for them, based on the data and additional personal customer information, and can converse with them in an appropriate tone. The bank can further hyperpersonalize the chat to make it more appealing to the client and improve the odds of the conversion, or add guardrails to minimize AI hallucinations and improve interaction accuracy. Example customer #1: Olivia Olivia is a young client requesting a credit card. The agent looks at her credit card history and annual income and recommends a card with low fees. The tone of the conversation is casual. When Olivia asks for more information, the agent accesses the card data while retaining the same youthful and fun tone. Example customer #2: Miss Jessope The second example involves an older woman who the agent calls “Ms Jessope”. When asking for a new card, the agent accesses her credit card history to choose the best card based on her history. The conversation takes place in a respectful tone. When requesting more information, the response is more informative and detailed, and the language remains respectful. How does this work under the hood? As you can see from the figure below, the tool has access to customer profile data in MongoDB Atlas collection bfsi.user_data and is able to hyperpersonalize its response and recommendations based on various aspects of the customer profile. A RAG process is implemented using the Iguazio AI Platform with MongoDB Atlas data platform. The Atlas Vector Search capabilities were used to find the relevant operational data stored in MongoDB (card name, annual fees, client occupation, interest rates, and more) to augment the contextual data during the interaction itself to personalize the interaction. The virtual agent is also able to talk to another agent tool that has a view of the credit card data in bfsi.card_info (such as card name, annual and joining fees, card perks such as cashback, and more), to pick a credit card that would best suit the needs of the customer. To ensure the client gets the best choice of card, a guardrail is added that filters the cards chosen according to the data gathered by the agent as a built-in component of the agent tool. In addition, another set of guardrails is added to validate that the card offered suits the customer by comparing the card with the optimal ones recommended for the customer’s age range. This whole process is straightforward to set up and configure using the Iguazio AI Platform, with seamless integration to MongoDB. The user only needs to create the agent workflow and connect it to MongoDB Atlas, and everything works out of the box. Lastly, as you can see from the demo above, the agent was able to leverage the vector search capabilities of MongoDB Atlas to retrieve, summarize, and personalize the messaging on the card information and benefits in the same tone as the user’s. For more detailed information and resources on how MongoDB and Iguazio can transform your gen AI applications, we encourage you to apply for an exclusive innovation workshop with MongoDB's industry experts to explore bespoke modern app development and tailored solutions for your organization. Additionally, you can enjoy these resources: Start implementing gen AI applications in your enterprise today How Leading Industries are Transforming with AI and MongoDB Atlas The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects Add vector search to your arsenal for more accurate and cost-efficient RAG applications by enrolling in the MongoDB and DeepLearning.AI course " Prompt Compression and Query Optimization " for free today.

July 24, 2024
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MongoDB Atlas Integration with Ably Unlocks Real-time Capabilities

Enterprises across sectors increasingly realize that data, like time, doesn’t wait. Indeed, harnessing and synchronizing information in real time is the new currency of business agility. Enter the alliance between MongoDB and Ably—a partnership that has led to Ably's new database connector for MongoDB Atlas . The new database connector provides a robust framework for businesses to create real-time, data-intensive applications that can provide top-notch user experiences thanks to an opinionated client SDK to be used on top of LiveSync, ensuring both data integrity and real-time consistency—without compromising your existing tech stack. The synergy of MongoDB Atlas and Ably LiveSync This new MongoDB Atlas-Ably integration tackles a fundamental challenge in modern application architecture: maintaining data consistency across distributed systems in real-time. MongoDB Atlas serves as the foundation—a flexible, scalable database service that adapts to the ebb and flow of data demands. Meanwhile, Ably LiveSync acts as the nervous system, ensuring that every change, every update, resonates instantly across the entire application ecosystem. The Ably LiveSync database connector for MongoDB Atlas offers a transformative approach to real-time data management, combining unparalleled scalability with seamless synchronization. This solution effortlessly adapts to growing data volumes and expanding user bases, catering to businesses of all sizes—from agile startups to established enterprises. By rapidly conveying database changes to end-users, it ensures that all stakeholders operate from a single, up-to-date source of truth, fostering data consistency across the entire organization. At its core, LiveSync is built with robust resilience in mind, featuring built-in failover mechanisms and connection recovery capabilities. This architecture provides businesses with the high availability they need to maintain continuous operations in today's always-on digital landscape. Moreover, by abstracting away the complexities of real-time infrastructure, LiveSync empowers developers to focus on creating features that drive business value. This focus on developer productivity, combined with its scalability and reliability, positions Ably LiveSync for MongoDB Atlas as a cornerstone technology for companies aiming to harness the power of real-time data synchronization. Figure 1: Ably real-time integration with MongoDB Atlas. Industry transformation: A real-time revolution This new integration has a number of implications across various sectors. For example, in the banking and financial services sector , the MongoDB Atlas-Ably integration enables instantaneous fraud detection systems that can promptly react to potential threats. Live trading platforms benefit as well, seamlessly updating to reflect every market change as it happens. Banking applications are equally enhanced, with real-time updating of account balances and transactions, ensuring that users always have access to the most recent financial information. In the retail industry , meanwhile, the integration facilitates real-time inventory management across both physical and online stores, ensuring that supply matches demand at all times. This capability supports dynamic pricing strategies that can adapt instantly to fluctuations in consumer interest, and it powers personalized shopping experiences with live product recommendations tailored to individual customer preferences. Manufacturing and mobility sectors also see transformative benefits. With the capability for real-time monitoring of production lines, businesses can implement just-in-time manufacturing processes, streamlining operations and reducing waste. Real-time tracking of vehicles and assets enhances logistics efficiency, while predictive maintenance systems provide foresight into potential equipment failures, allowing for timely interventions. The healthcare sector stands to gain significantly from this technology. Real-time patient monitoring systems offer healthcare providers immediate alerts, ensuring swift medical responses when necessary. Electronic health records receive seamless updates across multiple care settings, promoting coherent patient care. Efficient resource allocation is achieved through live tracking of hospital beds and equipment, optimizing hospital operations. Insurance companies are not left out of this technological leap. The integration allows for dynamic risk assessment and pricing models that adapt in real-time, refining accuracy and responsiveness. Instant claim processing and status updates enhance customer satisfaction, while live tracking of insured assets facilitates more accurate underwriting and expedites the resolution of claims. Finally, in telecommunications and media this integration promises buffer-free content delivery and streaming services, vastly improving the end-user experience. real-time network performance monitoring enables proactive issue resolution, maintaining service quality. Users can enjoy synchronized experiences across multiple devices and platforms, fostering seamless interaction with digital content. Today's business imperative As industries continue to evolve at a rapid pace, the integration of MongoDB Atlas and Ably LiveSync provides a compelling way for businesses to not only keep up but lead the real-time revolution. For IT decision-makers looking to put their organizations at the forefront of innovation, this integration turns static data into a dynamic driver of business growth and market leadership. Access MongoDB Atlas and Ably LiveSync Resources and start your journey towards real-time innovation today. Learn more about how MongoDB Atlas can power industry-specific solutions .

December 18, 2024