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 Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024