Hello everyone. My name is Amit Ara. I'm software engineering manager with more than 20 plus years of experience. Today I have pleasure to talk about how we can engineer a real time translation platform system that allow truly seamless communication across languages and why engineering behind them is just as important as the AI model themself. Interconnected world where conversation happened across borders every second. IT language is still one of the biggest barrier to collaboration, learning, and even safety. My goal is to show how scalable AI Infras can bridge that gap and why translation is moving from being a convenience to becoming a mission critical infrastructure for our digital economy. Okay, so let's first set the stage. So real time translation platform are not just research experiment anymore, they're becoming essential part of how people communicate globally. The expectations are very high. So we are talking about processing millions of conversation daily. We have to support more than 40 plus languages. We have to hit transition accuracy above 85% and all of this in. A very small response time of under 200 milliseconds. So traditional translation tool simply cannot meet the scale and the latency requirement we're talking about, which is where the next frontier lies at the intersection of the advanced AI models and robust system engineering. To make sure that we are able to build this platform must address four interdependent imperatives. The first is performance. The latency has to be very low and with a very high throughput, users shouldn't feel any lag in such a system. The second one is the scalability. We have to support millions of concurrence sessions across the globe. And the system. And this platform has to be very reliable. It has to be available even during failures or heavy loads. And finally, the accuracy, and it has to preserve the context, making sure that meaning is not lost. People might use idioms, they might use slang. They switch languages during mid sentences or speak in mix of languages. To build this platform. The challenge is not to optimize just one of these things. All four of these should work together really well to make sure that platform is production ready and is usable. So fun part. Let's talk about building this one. So first let's talk about the architecture. So the real time translation systems are typically organized as the modeler microservice. Each one does one thing, but does it really well. So if we talk about the workflow. So speech first goes through your automatic speech organization to become text. Then we do pre-processing, which normalize the text. After that, we use neural machine translation, convert insert to the target language, and then post-processing cleans up. And finally we will have text to speech that generates natural audio output. So we will keep each of these steps separate in and so that we can independently scale, we can upgrade them, we can monitor these components without breaking the entire pipeline. So the modularity is the backbone of the performance and throughput optimization. So now coming to the deployment, how are we gonna do that? So models are packaged into containers and we will them by platforms like Kubernetes. This will give us version control. We can run domain specific models side by side with a channel purpose one. It also enables safe deployments. We can use bluegreen strategies. We can run new and old models in power and roll back instantly. If something goes wrong, we can employ Canadian releases. We gradually rule out updates to small percent of the users. See how it. Does performance wise, and then if everything is well, we scale it to everyone. So this will ensure we never disrupt mission critical use cases. So this is very important that centralized cloud processing is not enough. Imagine a user in India or Africa need, they need a sub 200 millisecond latency, and if you're sending everything back to US data center, we'll never meet our requirements. So that's where edge computing comes in. We will deploy all these moder components, A SR and MT ETTS components closer to the users. We can reduce the latency by 40%. We can cut our upstream bandwidth usage by almost 60%. And most importantly, we keep the sensitive data within the local judication, which helps with the regulatory compliance each country has. So if you think about this, edge computing isn't optional or nice to have for the system, it's a fundamental part of the system. So next is data and hardware. On the data side, we use streaming pipeline like Kafka and flank for load latency, ingestions and batch pipelines for retraining and analytics. So on the hardware side, the GPUs are the bottlenecks, so we have to make sure that we do efficient GPU scheduling so the multi multiple models don't collide during peak loads. And we use caching aggressively to, for the common phrases, domain specific terms, user dictionary to speed up translation while improving accuracy. This combination keep the system both fast and the cost Absent now, like none of this will matter if you don't know what's happening in your production system. So telemetry is a nervous system, is the most important part of your platform. We have to track metrics like latency. We should track throughput, accuracy, error rates. We use dis tracing to pinpoints. What are the bottlenecks across microservices. And we then we feed this user correction back into the system to improve accuracy over time. We have. For full tolerance, we design for ancy across regions. We allow graceful degradation by falling back to simpler models. When cheap use are scars, we build self-healing, automated restart load balancing when the service fails. So residency has to. Be engineered, designed, and from the start of the platform, we have to incorporate those. And it doesn't have to, it's not accidental. So it has to be very intentional. It has to be in the start of the system and it has to engineer it from in the whole platform. So another fun thing let's look at some of the real world impact. Of this real time translation platforms. So in healthcare if there's a miscommunication between provide and patient, it can be a matter of life and a death. So a hospital system deployed domain specific medical translation models at the edge nodes inside hospitals. And as far as the impact is concerned, a 35% reduction in critical medical communication errors that directly improve patient safety and treatment outcomes. This is a clear example where translation literally saves life. No. We all have seen this one as education. In online classroom we have diverse student population. They often of, they often struggle with the language barriers. So by integrating real time captioning and translation into the video conferencing platforms, students are. Were able to fully participate regardless of the native language. The results were more inclusive accessible learning environment, and there's no student left behind because of the language barriers. So finally coming to the enterprise Collaboration. So global team lose efficiency to the language caps. You're having meeting across international teams by embedding, so by embedding real time translation directly into the platforms like Teams and Slack. Your organization can see more than 40 plus improvements in collaboration effectiveness. This can translate to faster decisions, fewer misunderstanding that happens, and better outcomes for international projects. So from these use cases and from the the previous slides and previous experiences. So these are the best practices that emerge. First is like you design for ity. You always do one thing and you do one thing really well. You're able to independently scale individually update that. Components like separate a SR and mt TTS we talked about. Here's hybrid cloud strategy as well as. Edge architecture, which is a must have for real time translation system. You automate your deployments using CICD. You have to prioritize, and that's a must have observability. With the full tracing and feedback loops, you have to plan it and see from the start assume this will happen. Plan for it and make sure it's implemented. Like GPU scar. So you have to optimize for GP usage, you have to aggressively use caching. And I think one of the most important thing is you have to support contextual translations. Because the meaning matters more than the literal words substitutions. So at the high level, these are the principle that make translation platform reliable at scale. Okay, future direction. Looking ahead, the possibilities are exciting. Imagine we can build personalized translation models, which are tuned to individual users and organizations. We can. Create those context aware nmt that uses conversation history to improve the fidelity. We can have the ed learning at the edge that allow training without sending the con the sensitive data, which you don't want to send to the cloud. We can have multi moderate translation. We can combining speech, text, even visual will unlock such a richer understanding. And we must prioritize sustainability, optimizing g pure workloads, full lower energy consumptions. So the future of translation is intelligent, adaptive, and green. So let's bring it all together. So engineering realtime translation platform is both an AI challenge and a system engineering challenge. It requires low latency, pipelines fault and architecture, T resource management, robust observability when done right. These platforms saves life in healthcare, democratize educations, and ate global collaboration. So overall translation is not just convenience anymore, it's infrastructure. For digital, thank you so much.