Hey. Hi people. Good morning everyone, and thank you for joining me today. My name is Linga Ella, and I am from Fairfield University. It's pleasure to be here at conference 42 JavaScript 2025 sharing insights on one of the most transformative evolutions in in frontend architecture and how it's fast tracking a integration in modern web applications. Over the past few years JavaScript has become the backbone of nearly every digital experience. From the simplest landing page to complex AI powered platforms. But as our application has grown so have the challenges, particularly when it comes to embedding artificial intelligence in, into existing and large scale systems. Today, I will walk you through how the micro front runs can break those barriers, helping teams innovate faster, deploy smarter, and deliver AI features independently without being slowed down by monolithic architecture. Let's go into the first slide. In today's digital landscape the users expect applications that are intelligent, responsive, and constantly evolving. Whether it's personalization, predictive analytics are the nature language chatbots. AI has become the central to modern user experiences, but here is the challenge. Most frontend architectures weren't designed for this kind of rapid evolution coming to the traditional monolithic front ends. It forces every team to work. Within the same reliable cycle. Adding a new AI powered feature often means touching code basis of a shared code basis, or retesting the entire application or regression testing and waiting for the full scale deployments. The result, it will slower our release cycles or break dependencies. And miss opportunities for innovation. And as the organizations try to scale AI capabilities, these bottlenecks become even more painful. That's what the micro frontends come in. Let's go to the next slide. So here what are the micro front ends, right? The micro front ends take inspiration from microservice revolution on the backend. Instead of building one massive JavaScript application, we decompose it into smaller, independently deployable units, each owned by specific team or domain. Think of it as a federation or self-contained front ends, right? Each with its own lifecycle technology choices and release schedules. Coming to the benefits of these micro front ends. They're immediate, right? So the teams can deploy independently without waiting for this. Centralized release. And developers gain autonomy focusing deeply on the feature specific feature areas. And they have technology freedom. Let's say if you have to choose between different frontend frameworks, right? Such as such as react View, angular, or whether it is vanilla, plain vanilla, JavaScript. You have that freedom Micro frontends essentially bring modularity and agility to the frontend world. And that opens new doors for the AI integration. Let's go to next slide. The AI integration advantages right? So what are the advantages? Let's connect the dots here. When teams are free to develop and deploy independently, they can introduce AI capabilities without touching the core application code. Let's say for example, a chart bot it, a chart bot can be, can deploy conversational AI as a standalone micro front end. And a recommendation engine. A team can integrate personal personalization independently. A data analytics team can build predictive dashboards without disrupting the other workflows. So each AI feature can evolve at a, at its own pace, using their own stack. And even run experiments in isolation without affecting the core code base. This autonomy allows organizations to deliver ai mul multiple three times faster, test more variations, and adapt quickly to what user actually responds to. That's the beauty of this model. Faster innovation without coordination bottlenecks. And yeah, this is the real world impact which I covered earlier. It's three times faster and the results speaks for themselves, right? Like the companies adopting micro frontend architecture for AI report. Up to three times faster, the feature delivery cycles, and they have seen a 45% increase in the user engagement driven by rapid a experimentation. And their AB testing speeds have grown by nearly 10 times, allowing them to compare multiple AI variations simultaneously. When the team no longer waits on each other experimentation, flourishes, right? And that's where the AI thrives here. Module Federation with Webpac five. This is how we are going to implement. This is come coming to this technical foundation. Now let's explore how this works technically. The foundation of most modern front-end frameworks setups is module Federation introduced with Webpac five. So Module Federation enables different applications. Even built with the different frameworks to share code at runtime. You can expose a component from one project and consume it dynamically from another project without rebuilding or redeploying. Here is how the basic flow works, right? You can configure the Model Federation plugin and define which module to expose. And you can expose your AI components. Say you say recommended recommendation wi our chart bot consume that module remotely in another front end. And and also we can share common dependencies like reactor view or, optimize bundle size. This setup allows truly distributed development while maintaining seamless user experience. This is the edge side composition techniques coming to this these techniques. The next major enable is said composition. It's a performance focused approach that assembles micro frontend at CDN Edge, right? Traditionally the composition happens on a client which can increase initial load time. But the ED side composition the CDN stitches together only the necessary. Microfund for each route. Whether it's serving a lighter, faster bundles bundles to the user this improves time. Time to interactive enables independent caching and delivers an instantly responsive experience across the distributed networks. In simpler terms, the user gets what they need from the nearest edge node, the movement, they need it. This will reduce the, bundle size and make the applications faster. This is how we do it. And next step is web assembly integration for ai. So coming to this technique one of the most exciting advances in is how the web assembly or WASM brings machine learning directly to the browser by compiling AI models from frameworks like transfer flow or pie chart into web assembly. We can execute interfaces on the client side, achieving near native performance as what we do, what we usually do on the backend side. This means complex task like predictions, image classification, or the sentiment analysis can run instantly without. Round trips to the server. And it's not just faster, but it's also privacy friendly and cost efficient, right? Since the sensitive data never leaves the user device, imagine delivering a fully functional recommendation engine directly through a micro front end. Powered by web assembly now, the API integration strategies. So while the web assembly handles local local inference, most AI systems still rely on a PA for deeper intelligence. And micro frontend can integrate these APIs independently allowing fine grain control and versioning. We we typically see two major patterns. One is the rest, API integration. Another one is GraphQL Federation. Coming to this a rest API integration. We have standard HT DP endpoint that each micro function manages independently. Teams can evolve their AI services, update models or handle retrieve without affecting the other services. And coming to this GraphQL Federation where multiple micro front ends. Contribute subgraphs for to form a unified schema. This enables type safe queries that span multiple AI services without precision reducing network overhead. Together these patterns make AI communication modular and scalable. It just like the architecture itself. And next step is state management across front ends. This is one of the most important pieces in the front end architecture. So here one of the biggest concern is distributed front ends in state is state managements. And, how do we maintain consistency when multiple micro frontends share the same or user context? So the solution lies in layered approach. Let's say we can keep local state within each micro frontend whenever possible. Or for shared state, we can use custom events or the lightweight shared store. Are, maintain consistent UI updates through optimistic rendering patterns, right? And this approach allows each component to remain isolated, yet responsible to global events, maintaining a seamless user experience, even as the AI features are dynamically loaded or replaced. Coming to the performance optimization techniques eventually the build size we are trying to reduce the build size by decoupling the monolithic application into micro front ends. And then also we can we don't have to load everything at once in the front end that makes the browser slower. So we have four techniques consist consistently deliver results. So the first one is the lazy loading A components dynamically import features on demand to mini minimize the initial load. And efficient resource sharing. So we, if you have common components or dependencies, we can reuse them to prevent the code duplication and advanced caching. So use service workers to cache the API responses are predictions intelligently, right? The next one is serverless deployments. Deploy via the edge networks and JavaScript run times for global reach, right? And with all these techniques together, these strategies keep the distributed front ends performing like the single cohesive application. It'll be smooth, fast, and reliable. This is the implementation roadmap. So now that's, we have covered the what and the how. Let's look into the implementation journey, right? So assess your current architecture. So identify monolithic pain points, especially where the AI integration is blocked. Define clear boundaries like organize around business domains ensuring each team owns a meaningful slice of functionality, let's say features, right? Implement module federation set up shade, dependency management, and deployment pipelines. Integrate your first AI feature. The key here is start small, maybe a chart bot or a analytics widget, and use it as a proof of concept scale and optimize. Gradually once once you get a taste of it, then you can gradually expand, measure performance, and it rate continuously. Here the key is to start small, learn fast, and scale confidently. These are the key takeaways. To summarize micro front ends fundamentally change how we approach AI integration in modern web apps. They remove independent bottlenecks, allowing the teams to deliver a capabilities rapidly and independently. They empower specialized teams to experiment, iterate, and innovate without waiting on centralized approvals. And with technologies like module Federation and edge side composition and web assembly, they deliver exceptional performances and flexibility. Whether you, your tech stack is react, view, angular, or plain JavaScript, these strategies can transform your architecture and accelerate your AI roadmap. I am experimenting with these technologies to create an e-commerce application, which will cover the user application and also the admin application for the store. And also we have, we are going to develop a driver app, which will evolve, which will encourage the e-commerce applications. It is work in progress, but eventually we will get there and yeah, we'll, it'll be production ready in few months from now. And coming to this closing points. As a, as developers, our mission is to build systems that can evolve as fast as our ideas. Micro sentence give us that agility, allowing every team to contribute intelligence, creative, and innovation without friction. As you head back to your projects, think modularly, think independently, deployable. And think of how each microfund can become a building block of our next AI driven experience. Thank you all for your time, your attention and your curiosity today. I am ling from Fairfield University and it's been pleasure sharing this with you at conference 42 JavaScript 2025. Hopefully we'll catch you some other time in the future. Have a good rest of your day. Thank you.