Hey all, thanks for being here. My name is Jimmy Kaar and I'm a senior manager at C-X-M-U-S-A. Today I'll be talking about building scalable ML ops pipelines for enterprise systems. This topic is critical because most machine learning projects don't fail due to poor models. They fail when trying to scale them in production. I'll share how at C xm, we build systems that can support millions of daily transactions reliably. The aim is to provide you with a mix of theory and practical insights that you can apply in your own organizations. Here's our agenda. We'll begin with the challenges enterprise face. In deploying ml, I'll show you why traditional IT deployment approaches, uh, break down when applied to machine learning. Next, we'll dive into the architecture fundamentals. These are the building blocks that every production ready EML systems need. Okay, then I'll explain the implementation framework, a step-by-step way to automate and streamline deployments. After that, I'll share some real world impact metrics from Sirius xm, so you can see the measurable benefits. Finally, closing with the strategic roadmap so you know how to bring these practices into your own organization incrementally. Now let's talk about the enterprise ML challenge. Many organizations still rely on manual processes. Deployments are done by hand. Environments don't match between development and production, and teams work in silos. As a result, the deployments can take months, incidents occur frequently, and compliance is difficult. This isn't just a technical issue, it's a business one. When it takes 60 to 90 days to put a model into production, you lose agility. Competitors can trade faster with. And will outperform you. So the gap between data science, experimentation, and production requirements become a major obstacle. Here we see the ML Ops maturity journey at Level Zero. Things are completely manual. Deployments are ad hoc, and monitoring is almost non-existent. Level one introduces some partial automation, maybe a few scripts, or a basic CICD setup. At level two, you can start continuously delivering models with pipelines and some monitoring. Finally at level three, everything is automated. You have self-healing systems and robust monitoring in place. At Sirius xm, we went from level one to level three in just 18 months. That shift reduced deployment times from weeks to ours while ensuring stability for millions of daily transactions. These are the four pillars of ML Lops Architecture. First automated model Val. Validation ensures only high quality compliant models get deployed. Second, CICD pipelines, standardized deployments, and make rollbacks easy. Third, monitoring systems keep track of model performance, data drift and overall system health. Finally, scalable infrastructure such as Kubernetes allows us to manage workloads dynamically. Together. These pillars are what make scaling to hundreds of production models possible. At Series xm, we built an automated validation framework to ensure that only production ready models make it live. This framework ensures models on three dimensions, statistical performance, operational readiness, and compliance. Statistical checks cover accuracy, precision recall. F1 score and business KPIs. Operational readiness checks, latency, scalability, and throughput compliance ensures explainability bias detection and regulatory alignment. By putting these gates in place, we reduce production issues by 94%. That's a massive improvement in reliability. This diagram illustrates our CICD pipeline. It starts with version control, not just for code, but also for data sets, configurations and artifacts. The build and test stage uses containerization, performance benchmarks, and security scanning. Staging is where we do shadow deployments and AB testing to ensure the model behaves correctly under load. Finally, in production, we rely on candidate deployments, automated rollbacks and audit logs. The result we brought deployment times down from 45 days to just four hours. That's the power of automation and a standardization. Monitoring is essential because models degrade over time. We monitor across four dimensions. Model performance, data quality. System operations and business impact. That means looking at accuracy and KPIs, checking for data drift, watching latency and error, and finally, measuring revenue or conversion impact. In the first year alone, our monitoring system detected 28 critical drift incidents before they could affect customers. This kind of proactive monitoring helps prevent costly business disruptions. Enterprise ML workloads are unpredictable, so infrastructure has to be both powerful and flexible. We use Kubernetes for container orchestration and autoscaling Apache Spark for distributed data processing and load balanced interfer interference services. For ancy security is equally important. We enforce strict access controls, encryption, and audit logging. As a result, our system now handles 3.5 million inference requests daily with 99.99% uptime and sub 50 millisecond latency. That's production grade infrastructure at scale. Of course, technology alone, alone doesn't solve everything. Organizations fail face challenges like mismatch skill sets. Fragmented tools and inconsistent success metrics. Often engineers and data scientists simply don't speak the same language. The solution we found at CSXM was to unify development environments, standardized workflows, and create shared KPIs. Cross-functional collaboration was key. In fact, our VP of AI engineering said the biggest win wasn't technical. It was getting both sides to speak the same language. This chart compares metrics before and after M lops. Before deployments were slow, expensive, and error prone. After implementing M Lops, deployment timelines dropped dramatically. Incidents were fewer, iterations became easier, and costs went down. The takeaway is that ML Ops isn't just about engineering. It directly translates into business efficiency and agility. Here's the roadmap we recommend for implementing ML ops. Phase one is about laying the foundation, standardized environments, version control, and basic pipelines. Phase two introduces automation with CI c. D pipelines, validation dashboards, model registry, and metadata tracking. Phase three scales. The system with Kubernetes orchestration and advanced monitoring, along with data drift detection systems and establishing automated rollback mechanism. Finally, phase four focuses on optimization, implement advanced features, stores self-healing infrastructure, features, stores restraining and governance. The key is not to attempt a full transformation overnight, but to deliver increment values. In phases. Now let's look at common pitfalls First, don't become tool obsessed. Tools should serve workflows not the other way around. Second, don't underestimate the cultural cha. Change required. If teams don't adapt, the initiative will stall. And third, don't over complicate your first steps. Start small, prove value, and then scale. The most successful implementation, start with a single high value use case and expand from there. To summarize, ML Lops provides a real competitive edge. Organizations that do this well can deploy models 20 to 30 times faster than their peers. The four pillars, validation, pipeline monitoring, and infrastructure are your foundation, but equally important is bridging the cultural gap between teams. Finally remember, start small, focus on value and scale quickly. Once you have proven success, this approach will maximize both technical and business outcomes from your ML investments. That brings us to the end of the session. I hope you're walking away with not just theoretical knowledge, but also practical steps for building scalable ML lops pipelines. Thank you so much for your time and attention and have a great rest of your day.