Hello everyone. Welcome to conference 42, machine Learning 2025. My name is Baba PRIs ela and I work as a staff technical program manager at Walmart, where I primarily lead infrastructure, engineering and DevOps teams. So today I am honored to speak to you regarding. Architecting intelligent platforms, how machine learning is reshaping modern enterprise infrastructure. How machine learning is transforming cloud platforms into intelligent systems that deliver measurable business value. So let's jump in. So let's talk about evaluation evolution of cloud architecture. Let's start with how cloud infrastructure has evolved. Initially, we had traditional infrastructure, manual provisioning, fixed capacity, and reactive management. Then came cloud migration, which offered. Elasticity, scalability and automation, but the real leap forward is the machine learning integration. This brought us intelligent infrastructure. Systems that predict, optimize and self-heal. Self-healing is a big thing in the contemporary business because all the enterprise technologies, all the enterprise companies or the businesses today, they are looking for the systems which can self-heal. So today, as I said, we are entering the era of intelligent platforms, infrastructure. That continuously optimizes itself delivering resilience and efficiency by design the new cloud architect. Now this transformation demands a new breed of architect. So today's cloud leaders must blend. Strategic machine learning integration, aligning machine learning with business goals, so aligning machine learning capabilities with core business objectives and the value creation, advanced machine learning engineering. So we have to ensure that designing and implementing sophisticated machine learning models. Optimized for cloud environment infrastructure mastery. That's very important. Leveraging deep cloud architecture expertise as the essential foundation. So modern cloud architectures must develop a sophisticated understanding of machine learning. Alongside traditional infrastructure expertise to create a very functional and very true intelligent platforms that deliver measurable business value. Let's get onto the next slide now, machine learning frameworks for cloud. Now let's look at the building blocks. There are four key frameworks powering this transformation, TensorFlow on cloud. TensorFlow. It dynamically scales complex machine learning workloads across distributed cloud infrastructure, which enables enterprise grade model training and deployment. I. Cube flow where Kubernetes is one of the trending things in the market. So cube flow, which orchestrates end to end machine learning workflows on Kubernetes, streamlining model development training, and serving in containerized environment, machine learning ops platforms. So this is very critical because it automates the entire machine learning lifecycle from development to production, ensuring reproducibility, governance, and continuous model monitoring. And the final one is the Cloud ML services, which delivers production ready APIs for vision, language, and predictive analytics tasks. Accelerating time to market without extensive machine learning expertise. So as I said today, accelerating acceleration, innovation, and ready to move already to use APIs. That's the vision, that's the language, and that's the production. So together. They may democratize machine learning, making it scalable and manageable within cloud native environments. Let's get onto to the next slide. Governance for success Now, but innovation without governance invites chaos, as you all know. So a miss. A successful machine learning platform demands standards for architectural consistency. So it defines standards. We need to ensure that we are establishing comprehensive guidelines and architectural principles for machine learning implementations across the enterprise. Iterate and improve. No leverage. The outcome data to continuously refine machine learning, implementation strategies, and governance frameworks ensure conce compliance is very important because implementing robust monitoring systems to verify adhering to security protocols, regulatory requirements, and ethical AI standards is very important. Measure outcome. So there should be metrics out of it. So deploy comprehensive metrics to quantify business value and written on investment of machine learning initiatives against strategic objectives. Let's go to the next slide. Now let me bring this to life with a healthcare use case. So what are the challenges a provider faced a 30% readmission rate and only 65% diagnostic accuracy. Now, what will be the solution for this now? Cloud-based machine learning algorithms, which are integrated with existing EHR systems. That enables real time clinical decision support and predictive analytics. How should the implementation and what will be the results now deploying machine learning models? Analyzing patient vitals, lab results, medication data, and social determinants of health to predict deterioration, risks and recommend interventions. It also helps in implementing cloud-based machine learning, as I said, which are integrated with EHRs that flags the risks early. You know the results. Readmissions dropped 47%. Accuracy jumped to 92%, and the most importantly, savings exceeded 3 million annually. That's the power of machine learning applied strategically. Let's go to the next slide now. Machine learning value creation. This is not theory. This is measurable. 42% improvement in operational efficiency. So when you enhance resource utilization across cloud infrastructure, that's the value. That's the improvement we have shown. 67% is true resolution Now. When accelerated anomaly detection and automated remediation is in place, we will be able to resolve the issues much prior. So that's the number, that's the percentage which we are seeing. 67% faster issue resolution, 31% reduction in cloud costs in the contemporary market where the businesses. And the enterprises are looking for optimization to save money. Optimized cloud spending through intelligent resource allocation is helping to reduce costs, and most importantly, 3.5 x faster innovation cycles now accelerated time to market for business critical. Capabilities. So these numbers are real world outcomes from enterprises investing in intelligent infrastructure. Now human machine collaboration. Let's talk about, let's dive into the types of machine learning now. Supervised learning, which powers anomaly detection and predictive resource planning. So engineers. They provide labeled data, enabling models to learn from established patterns and expert knowledge, real-time infrastructure, anomaly detection, intelligent cloud resource optimization, predictive capacity planning, and forecasting. They are the benefits of supervised learning. Now, unsupervised learning also helps us a lot. It helps us to discover hidden patterns for performance optimization. It the systems autonomously discover hidden patterns within cloud telemetry without explicit guidance. That is wonderful. Uncovering non-obvious system correlations and performance clustering for targeted improvements and workload segmentation for optimization. The last one, which is really crucial, the reinforcement learning. It gives us self-healing systems and dynamic security responses. It saves lot of resources, algorithms, which iteratively improve operational decisions by balancing exploration with exploitation, dynamic infrastructure, auto-scaling. Self-optimizing network configurations, adaptive security response. These are the vital things of reinforcement learning. This is the future. Humans guiding machines and machines learning from operations. Let's get onto the next slide now. What should be the organizational readiness for this? Success hinges not just on tick, but on the organization's readiness. What do you say? Awareness on machine learning's potential now by cultivating organizational understanding of machine learning capabilities and strategic opportunities for business transformation. So we need to ensure that we create this kind of awareness for machine learning's, potential skill development. Through upskilling and hiring. So investing in talent acquisition and upskilling existing staff to build robust internal machine learning cap competencies or infrastructure preparation with scalable infrastructure. What can be done? No. We need to understand what is the existing infrastructure we have establishing scalable. Technical foundations with appropriate data, architecture, processing capabilities and integration points, and. One should have clear implementation plans that aligns with the business. So executing strategic machine learning powered initiatives with clear business outcomes measurement framework and continuous improvement circle cycles. It by which by doing all these things, we can ensure that the. Organizational readiness is met, and we are on the way to adopt intelligent learning and artificial intelligence. Now, let's get onto the next slide. For every new thing, there will be challenges. There will be implementation challenges, and let's be honest, it's not easy. So there will be key challenges. Key challenges would be the data quality. Meaning like garbage in, garbage out. Poor data integrity severely undermines modal performance established. We need to ensure that we are establishing comprehensive data governance frameworks and advanced pre-processing workflows to ensure reliable inputs for machine learning systems integration complexity. Legacy systems, they create friction. So entrenched legacy infrastructure creates significant integration barriers. So make sure we deploy modern APIs and robust microservices architecture, which can seamlessly connect machine learning capabilities with existing enterprise systems. Skill shortages, no machine learning talent is cars. So ensure that critical machine learning talent remains in high demand at short supply. So develop targeted upskilling programs and implement strategic recruitment initiatives to secure essential expertise across data science and engineering domains. As I said, critical machine learning talent remains in high demand. But we have a short supply, so make sure that we are developing targeted people with upskilling programs and ensure to implement strategic recruitment initiatives change resistance. Cultural inertia can slow progress, as you all know. So organizational inertia, frequently derails machine learning adoption efforts. Ensure that we are crafting comprehensive transition strategies, highlighting tangible business outcomes and establish executive champions to drive cultural transformation. So each of these must be tackled head on with governance, communication, and training. Let's get onto the next slide. What should be the steps? How can we achieve this? What, where do we start? How do we assess the current state? What's ready, what's not? We need to ensure we are evaluating infrastructure readiness and machine learning potential. Identify high value use cases. Then develop a strategic roadmap. Don't just do ml. Define where it delivers value. Meaning we need to create a roadmap with prioritized initiatives. Establish governance framework, which is going to help us to develop a strategic roadmap. Start small with high value pilots. Now for us to grow big, we need to start small then, so then scale up fast using repeatable frameworks. Begin with targeted pilots, document learning and expand successful patterns. Make sure that. We are documenting our success, our failure, our learnings that will help us to have successful patterns going forward. Now with this I wrap my session. Machine learning is not just enhancing cloud infrastructure, it's reinventing it. As cloud leaders, our role is not just to adopt technology, but to engineer intelligence. Into the core of our platforms. Thank you for your time today. I hope everybody liked my session. Thanks a lot.