Good morning, afternoon, evening, everyone, and welcome to my session, ML Ops at Scale Production Ready AI Systems for HR Transformation here at ML 42 ml OPS 2025. My name is Ram PRIs Miana. A little bit about my background as we begin. I have over 20 years of work experience in the technology space working at top companies such as NSF International, Abu Dhabi Investment Authority, and. Oracle. I'm currently serving as an Assistant director of HR Information Systems at NSF International. Throughout my career, I've been instrumental in driving digital transformation, AI and ML integration and enterprise technology architecture. My expertise spans HR technology modernization cloud ERP implementations and AI driven automation across various industries. These experiences have given me a deep understanding of how innovative solutions can enhance operational efficiency and deliver tangible business outcomes. Today I'll be sharing a little technical blueprint for deploying generative AI in HR Shade Services Center, focusing on not just operational excellence, but trust, compliance, and the ability to scale. Let's dive in. Here's what I'll be covering in the next 30 minutes, right? First, I'll introduce our ML ops architecture and show you what it really takes to run a reliable production grade ML system supporting over 10 million HR transactions every month. Then I'll cover how we approach performance monitoring, including drift reduction and custom HR metrics. Keep in mind, this is critical for any regulated hr, high volume HR operations. Next, I'll walk you through our data engineering strategies, including building privacy, preserving, and real-time feature pipelines. After that, I'll break down our scalable deployment patterns, highlighting how we serve our 50,000 employees efficiently and securely across multiple business entities. And finally, I'll focus on our operational excellence and governance framework as to how we build audit trails, how we reduce bias, and how we can ensure compliance at every step. Let's talk a little bit about the HR AI Production Challenge. HR Shared Service centers present a unique set of challenges for AI operations, and we must process sensitive employee data that crosses national and legal jurisdictions, workforce size and needs fluctuate seasonally. Mass hirings performance review cycles and benefit open enrollments can spike query volumes dramatically. Models must deliver reliable, consistent results across a wide variety of HR query types. On top of that, strict compliance requirements in regulated industries are negotiable. Okay. All of this has to operate at massive scape, right? Over 10 million monthly interactions. Standard MOPS approaches just aren't enough for these high stakes and in an ever changing environment. So we need a customized architecture and continuous automation just to stay ahead. Let's go to the next slide. In this slide we'll talk a little bit about the production ML ops architecture. Our production environment is based on containerized model deployment orchestrated with Kubernetes. This architecture gives us a flexibility to optimize resource use and tap into GPU acceleration where needed for both training. And in France we use an automated AB testing and cannery deployment framework. This allows us to roll out new models safely and deploying to a small segment of users first, backed by statistical significance testing, so we never compromise on the employee experience scheduled. Automated retraining pipelines are at the core of our life cycle. Keep in mind that these key part models maintaining at least 92% accuracy even as business requirements and data distributions shift across a diverse range of HR query types, and the next slide. Next, let's talk about the ML Ops pipeline workflow here. Let's look at our ML ops pipeline designed to ensure not just speed and accuracy. But also the robust compliance. We start with data ingestion, streamlining the data from HR systems, chat bot logs, and ticketing workflows, and data validation is next. Applying strict privacy and schema checks right upfront. We dedicate significant effort to feature engineering, creating and transforming model features that truly reflect HR business logic. Like capturing tenure or internal mobility. Next comes the model training. It's tightly tracked and experiment managed for reproducibility. Every model goes through a rigorous evaluation and approval process, combining quantitative metrics with stakeholder reviews. It's a balance. This end-to-end workflow ensures we never sacrifice quality or auditability for speed. And the next slide, let's talk a little bit about the model performance and monitoring. This is the key. Monitoring sits at the heart for problem ready production readiness. We maintain real time dashboards that give granular visibility into every HR metric that matters, like chat bot response accuracy, average resolution time, or single satisfaction rates, or robust drift detection infrastructure spots, the changes. In input data or model output, very quickly recognizing the degradation, 73% faster than before. We track custom HR relevant metrics and use automated alerting to guarantee a 19 9.5 SLA for the business. Staying proactive means catching potential issues before the impact of real employees. We're not reactive. So now let's talk about the continuous feedback and improvement. As we can see in the diagram, we operate a continuous feedback and implement loop. All user interactions, every question, every chatbot response and every satisfaction rating are thoroughly tracked. We analyze fail interactions and patterns that flag friction or confusion. These insights flow directly into our supervised retraining cycles. Ultimately resulting in 28%, which is a big number boost to overall model accuracy. The lesson here is that HR AI systems should never be static. They should evolve as the workforce and the business needs change. In the next slide, we're gonna talk a little bit about the data engineering for HR ai. HR data requires the highest standards of privacy and data quality. We cannot compromise on that. We have fully automated pipelines for PII, detection and tokenization. These are built to comply with global standards like GDPR and CCPA. Data quality is protected by strict schema enforcement and anomaly detection. With our systems achieving a 99.2% data accuracy rate to keep things fast, we use real-time feature engineering with streaming architecture, cutting model inference latency by 65%. That means no compromise between privacy, compliance and user experience. Now this is the key. How do we implement it? What is in store for each feature? This is the key enabler, right in our pipeline, which is our HR feature store. This provides consistent features for both training. And real time inference with point in time correctness to prevent leakage, especially important for time series HR data. It's supposed low latency, less than 10 milliseconds feature serving for instant response in HR applications. And everything is automatically versioned and lineage tracked using Spark and Flink. The result is twofold. A 65% reduction in latency for end users and a 40% savings on computer infrastructure costs. This is a great outcome. Now, how can we scale this? Let's talk about the scalability plumbing strategies. So how do we operate at scale? Our multi-tenant, my, our, excuse me, our multi-tenant architecture isolates business units while sharing secure reliable infrastructure, which supporting over 50,000 employees with risk separation. For model updates, we use BLUEGREEN deployment, ensuring zero downtime and instant rollback If something goes wrong, our auto scaling infrastructure gracefully absorbs traffic spikes up to 10 times. During busy seasons, cost optimization is built in leveraging spot instances and dynamic right sizing to deliver a 40% cut in the infrastructure spend. Let's understand the graph about the seasonal scaling patterns. This is some real data. You'll see big spikes in query volumes and infrastructure costs corresponding to typical HR cycles. For example, onboarding in January, performance reviews in December, benefits enrollment in the fall. Our auto scaling setup dynamically adapts to these cycles, so resources are always available when needed, but we aren't over-provisioned and wasteful in quite periods, so we're saving some money there. If you only remember one thing here, keep in mind that you should align your technology scaling with your business and HR calendar. This brings me to the operational excellence in governance. Operational excellence goes hand in hand with governance. We've implemented a comprehensive ML ops governance framework tracking all AI decisions with end-to-end audit trails that enable rapid compliance responses. Automated bias detection is built in, and our approach has reduced discriminatory outcomes by 89% while maintaining high model accuracy. We offer multi jurisdiction compliance monitoring and model explainability tools tailored to HR so that the decisions impacting people's careers can always be interpreted and justified. Now talk about, let's talk about the bias detection and mitigation on the critical topic of fairness, which is our automated frameworks for bias detection, mitigation, and validation have been transmitted. We check for bias, both in training data and during ongoing model evaluation. Constantly monitoring and revalidating for each retraining cycle. Thanks to this, we've reduced discriminatory outcomes by 89% while maintaining model performance. Keep in mind that biases bias is not something you solve once. It's a continual responsibility for hedge, for every HR data practitioner. All right, so we reached the end of our presentation, so let's talk about the key takeaways and we can have a q and a to close. Let me summarize the key lessons. Always use containerized deployments and automated retraining to manage diverse changing HR workloads. Build HR specific metrics. Talk to your leadership, understand what's required, and also include drift detection into your monitoring workflows. The third one is bring, is bringing privacy and data quality to the core of your AI pipelines. You're dealing with HR data, so we have to vary. Careful about it. Always architect for scalability. Use multi-tenant blue, green deployment, auto scaling, and cost efficiency. It's all about the numbers and about all. Make comprehensive governance and bias reduction a pillar of your operation. We should always be running with trust, fairness, and compliance. This a non-negotiable. So finally, thank you for your attention and engagement. I hope you find these blueprints actionable for your own HR AI journeys. Thank you so much.