Hello everyone. Welcome. I'm Baba Prasad Penola and I work as a staff technical program manager at Walmart Global Tech. Today I am excited to talk to you about powered AI building enterprise cloud platforms. Our discussion will focus on the intersection of AI and cloud infrastructure with a unique lens on how the go programming language powers AI at enterprise scale. Let's ground ourselves first. AI is transforming enterprise cloud computing. It's enabling automation. It is helping us to have real time analytics and enabling us to ha take intelligent decisions. It enhances scalability, optimizes resource utilization, and also improves security posture with air driven insights. Enterprises are now able to streamline operations, cut costs, and drive innovation. Today, the unique focus would be on how GOs performance characteristics make it an ideal foundation for building these AI driven platforms. Let's know what Go is GOs compile Nature lightweight con via go routines. And efficient memory handling make it perfectly suited for scalable real time AI workloads. It allows us to build robust fault tolerant platforms that drive efficiency automation, and helps us to take intelligent decisions at scale. Now let's do a deep drive. Go plus ai, the perfect match. Let's talk or let's explore why GO and AI are a natural page for modern cloud environments. Go and ai. Why are the perfect match for building efficient, scalable, and resilient cloud platforms? Let's talk about the strengths of go performance and efficiency. Its garbage collection is optimized and the compiled nature, it gives us a near sea level speed concurrency model. Core routines and channels offer lightweight parallel processing, perfect for training or serving AI models. Scalability Go is bond for microservices. Its minimal overhead. Makes it ideal for. Scalable modular AI services. Now let's look at the AI requirements, high throughput data pipelines, which helps for processing large volumes of streaming data, scalable microservices. It enables and helps us to distribute workloads across clusters fault tolerant operations. It helps the system to self-heal or recover gracefully in case of any adverse events. So the conclusion here is very clear, goes architecture aligns beautifully with what modern AI platforms need. Let's get to the next slide. What is the enterprise impact we have now? When you put these things into practice, what do we actually see? A 42% reduction in system failures. Thanks to proactive monitoring and AI powered health checks, a 37% improvement in resource utilization. AI helps balance workloads dynamically, which is helping us to improve the resource utilization. A 68% success rate in AI implementation. When organization adopt structured governance model. This is a super number. These numbers aren't hypothetical. They are real examples from production environment. The key takeaway from here is go powered AI platforms are not only stable. But also more cost effective and easier to maintain. Let's get into the next slide. Technical integration. Now, what is needed to make it all work? Let's now talk about how we actually implement these systems. What are the key components we have? There are four key components, the Go Platform, AI libraries. Operational tools, and most importantly, the business value Now core platform, which is going to be your core engine optimized microservices. They use containerized go services to achieve blazing fast AI pipelines. High performance APIs go allows us to expose endpoints with low latency. Critical for inference AI libraries, which is going to be your intelligence layer. TensorFlow and Onyx Go has strong bindings to these frameworks for model execution ETL pipelines. You can build memory efficient ingestion pipelines with native go libraries operational tools, which is going to be the strong control system. Monitoring and logging real time observability ensures that both data pipelines and AI models, they behave predictably. Governance built in compliance features help track how models behave and how decisions are made. So a well integrated GO AI system. It is not only fast, but also reliable and auditable. The last one, which is not the least one, the most important one. Why it matters. Why should enterprises care? Because with Go powered AI systems, we unlock tangible business value. S meaning increased efficiency or improved efficiency. Faster model performance means real time insights, reduced costs, so less compute overhead, and smarter scaling will lead to lower cloud builds. Improved decision making. AI pipelines integrated with business logic. It helps us enable proactive rather than the reactive strategies. So goal isn't just a technical tool, it's a business accelerator. Now what are the skills that is required for success now to build these platforms successfully? Organizations need the right blend of talent and strategy. So three key components here. AI governance, ethical AI is no longer optional. Teams need to build frameworks that ensure transparency and fairness. Integration patterns, architecture must support modularity, performance and scale. The last one, go Expertise. We should ensure that the engineers need to understand the go routines, the concurrency and memory profiling to fully unlock go's power. It's not about just writing the code, it's about writing the right code at scale responsibly. Now, what is the. A framework that is available for GO Native ai. Let's talk about tooling now. How do we actually enable all this now inference libraries Go NAV supports high performance bindings for tensor flow and onyx making model deployments seamless. There are also lightweight go native implementations. For Lean Edge or microservice inference data processing with go, you can build parallel ETL pipelines and stream processing that auto scales with traffic. You get specialized structures to handle AI workloads efficiently. AI integration GO has robust connectors to AWS Azure and GCP. And it also supports multi-cloud architecture. You can implement fallbacks, throttling, and abstraction layers for resilience and portability. So these native frameworks are what turn go from a language into a true AI platform. Let's get into the next slide. Healthcare study Now. Let's look at a real world case. Healthcare iot devices capture real time patient vitals. Now, AI models running on a go powered backend. Analyze this data continuously. Health practitioners, they receive evidence-based recommendations with probability scores for personalized care. Over time, the platform learns from outcomes, improving future predictions. This isn't just a theory, it's happening now and it's changing how we deliver care. I, so let's get into the next slide. What are the clinical results that we have now? What is the measurable impact? Here are the results from the GO AI implementation in the healthcare system. A 42% reduction in readmission rates, so people are more healthier. 21% improvement in diagnostic accuracy, a 72% fever adverse event. So when we get to know what's happening, we can have strategic plans. So that is helping us to reduce adverse event, a 73% faster decision making. What needs to be done? So intelligent decision making is 73%. These metrics show how technology can move the needle, not just for operations, but for human lives as well. So AI plus Go. Isn't just about uptime, it's about impact. Let's get into the next slide. Complimentary intelligence. Now, complimentary intelligence, human plus ai. I want to emphasize something important here. The future isn't human or ai. It's human and ai. Now, human expertise brings. Intuition. Ethical reasoning. Contextual knowledge. Now, AI processing brings pattern recognition speed scale. The best results come from collaborative decision making and continuous learning where human insights refine AI models. And AI insights empower humans. So together we make smarter, safer, and more balanced decisions. Now, organizational readiness, technical infrastructure assessment. So before we get into that. Here's how to assess if the A is ready or not. Technical infrastructure, evaluate systems, evaluate data pipelines and skill level governance framework. Set clear rules for ai, behavior, compliance, and accountability. Pilot projects. Let's have small projects. Let's start. Prove the value fast. It helps us to take the decisions, enterprise wide deployment, rollout in phases, monitor, optimize, and then adopt. So successful transformation happens at the intersection of readiness, responsibility, and innovation. So let's get into the next slide. What are the key takeaways from this? go Performance Model is ideal for enterprise grade ai. Native AI frameworks make integration very seamless. Human AI collaboration yields the best results. Governance is very essential for ethical. S sustainable ai. So powered AI isn't just a vision, it's a framework for building smarter, faster, and more resilient cloud platforms. So I thank everyone for your time. it was very helpful. it had an insight on how. AI powered the synergy between GO and ai. It is not just technical advantage, it's a strategic one. Go offers the performance, simplicity and scalability that modern enterprise platforms demand. While AI brings intelligence, adaptability, and transf potential. When brought together with the right governance, architecture, and vision, the creative foundation for resilience, data-driven organization that can scale with confidence. Thank you for joining me today at Con 40 to go along 2025. I hope this session has sparked new ideas and inspired you to explore what's possible when performance meet intelligence. I. I'm Baba Prasad Penola. Feel free to connect with me afterward. I would love to continue the conversation. Thank you so much everyone.