Hello everyone, and thank you for joining my session. My name is Raju Shari. I'm a senior cloud and ML lops engineer with over 10 years of experience building large scale cloud and machine learning systems. Currently at Freddie Mac, I'm a senior cloud developer on the enterprise data science platform. Today I'm talking about scaling ML mops with self service platforms. Here's a roadmap for my talk today. First. We'll look at the evolution of ML operations, like how organizations move from satellite experiments to enterprise grade production systems. Second, I've introduced the cell service platform architecture and it's key components, integration patterns, and the go governance models that supports it. Third, we will discuss a measured impact and implementation roadmap, including case studies, metrics, actionable steps for organizations that want to get started. Finally, I will share future trends and strategic positioning, things like auto ml, serverless ml, and premise, which will reshape how financial services approach ML ops in the coming years. So by the end of the session, you should have a clear understanding of how cell service platforms can drive efficiency, governance, and innovation all at once. Let's begin with MLS maturity shift. In the financial industry, human operations has gone through a big transformation. We started with solid experiments. Teams build models in isolation, often method without path to production. Deployments are manual, slow, error prone, and how to reproduce later are inconsistent. Water worked. One laptop might fail in production. The governance was aocc with the little oversight of traceability. Overall, rever disability was limited. Making it hot to scale or meet regulatory requirements over time, this evolved into integrated platforms that generalize the pool. MLI cycle. These platforms bring consistency, automation, and governance while assuring compliance. This shift from acceleration to integrated platforms. Is it hot OPT task scaling ML mops in banking and financial services? Now that we've seen how ML operations was involved, evolved, let's talk about why self service platform matters. You see there is 71 point percent duction in the data preparation time because platforms provide standardized pipelines and reusable components. A 40%, 40% duction in training cost by optimizing compute. So the workload run on their. Instead of wasting resources like GPU and CPU 8 38 30 8% improvement in deployment success with indicated CACD pipelines and enrollments. But beyond the numbers, platforms transform. All financial institutions manage ML risk and compliance. They enable force innovation while maintaining strict regulatory standards. Now look at the code platform architecture. Strong architecture is the foundation for organizational ml at scale. It ensure the platform is stable, secure, and compliant. While supporting the MLI cycle and meeting the regulatory needs, the architecture has three layers. The first one is foundation infrastructure. Its scalable. CPUs and G Bs. Resilient shortage and secure networking organization must decide whether to go cloud or on-premises or hybrid. Here Kubernetes key role in standardization while strong data securities, essentially related industries. The second one is data and feature engineer management. Unified environment for data envision, processing, validation, versioning, and incentive feature. Short the feature short, ensure consistency and reasonability while keeping everything auditable. The third ml lifecycle management. This covers fermentation, draining, versioning, deployment, and continuous monitoring. It includes C, D pipelines, registries, testing, and observability. Ensuring models are high performing and compliant. So in short, provides the power data, provides a foundation and lifecycle ensures governance and control. Now let's put the piece together and work at the different architecture in financial services. A platform like this crucial, it helps tax in complexity. So data scientist can focus on models, ensure provisioning, compute, networking, or storage. So at the same point, it's built by compliance Andro the street controls, generates audit trials or in an inch and deployment and ensure separation of duties between developers, operators, and compliance teams. So when regulator ask how this model client, who approved it, my data was used, when was it? Is it updated? The platform provides answer answers in gently, so in stock, achi and operations for ensuring security and compliance. Now, let look the key components here. There are four pillars. First, content or situation ban schedules, ML workforce, customer resources behind jobs. So they CanBan consistently across environments. Scalability and isolation in multitenant setup. The second here is distributed compute, spa and DOS handle lodge scale data. They provide parallel computing and lineage tracking, so every transformation can be traced, which regulators demand. The third, the model registry and artifact stored all time models have with compliance metadata, who trained them when data was used and when they were uploaded. So prevent UN from going live and. CRYPTOGRAPHICS checks ensure models are not tampered with the fourth CICD integration here. Automated pipelines run test security scans and approval gates before deployment. This enables post consistent and complete releases. Together these four pillars make platforms scalable, secure, and auditable. Let's talk about governance layer. So up to this point, we talked about infras compute. Pipelines. The final layer that ties everything together is governance. So without governance platforms, these regulated industries simply cannot succeed. So it has three parts. The first automated policy enforcement, the platform enforces access controls at runtime, respect, model limits, and applies security. Security consistently. Even if a data scientist config misconfigured something, the platform prevent compliance issues. Second, compliance documentation. Model course lineage diagrams and validation reports were generated automatically. Instead of manual documentation. Evidence is corrected in real time as part of third audit observability. The platform capture, lack logs, monitoring data and explainability outputs. If a regulator ask, why did this model make a decision? The platform provides explainability immediately. The takeaway. The governments must be built in front of the stock, not voted on. Later here, self service experience, developer view, the goal is freedom to innovate quickly, but within the compliance boundaries. This can be shared in two ways. First, AB obsession with control. Developers use curated environments with pre-approved packages, so they avoid setup. The award setup delays, security risk. So templates given consistency across team for more, more flexible team allows custom environments without breaking guard guidelines. Second, seen as that flow with one click. Developers deploy models into staging complaints, check and documentation, and automatically integrated packing and monitoring health track performance issues between. For developers, the platform feed simple brand cost. Behind the scenes it enforces governance, compliance, and security. Now we talk about building versus buying. One of the most important question here is it should we build the platform ourself or by a commercial solution? There are three options here. First, in our development offers full customization and integration with legacy systems, but it usually takes 12 to 18 months and the query requires a team of specialized engineers, second commercial platforms. Faster time. Faster time to value, usually three to six months, and we include building complaints, but you make force vendor LOCKIN integration challenges. Third, the hybrid approach. Take a vendor code for speed, then add even one governance integration. These feature reach tion in six to nine months. For most financial organization, the hybrid approach is the best balance of speed, customization and complaints. Let's talk about future of mops. Strong foundations will sell highly platforms, governance, automation, but the architectures of the feature go further. It involves HML most workload closer to data sources. It reduces ency improving privacy and cutting cost for fraud detection and trading makes a big difference. Enterprise ml, it enables automated model selection and tuning. Its explainability built in so business users can build models safely. Serverless trainings, we shift to event driven paper use models, making process simpler and cost efficient while keeping governance intact. Privacy, preserving ml, its uses federated learning and encryption to train across silos without exposing sensitive data. Today's architecture choices should prepare us per this edge, native, automated, and privacy first future. We talk about engineer architecture. Traditionally, ML and finance has been cloud-based, centralized training and inference. It works, but introduce latency and require more into data to be to the cloud. The next step is hybrid training in the cloud inference at the edge, this registers latency for realtime use cases while keeping scalability. The ultimate stage is edge. Need two ml training across distributed devices with the Feder learning. Running models directly on device, discuss dis discuss latency by up to 40%. And hand. Hand has privacy. Since data never lease its source. The future will be hybrid and Azure. A two. Combining cloud scale and edge responsiveness. Let's talk about emerging trends. Financial ML ops first enterprise auto ML Automation with guard rails, automatic model selection, hyper tuning. But with explainability so regulators can audit out outputs. Second serverless PY is event driven paper computation ml that emulates heavy infrastructure while still enforcing governance code privacy, preserving ml. Its use ED learning and encryption to PY models without exposing sensor data. This enables collaboration with while maintaining privacy together auto ML for speed, serverless efficiency, privacy, preserving ML for complaints, create a blueprint of next generation ML ops. Here are the four. Here are key takeaways. First, platforms are so essential. Scaling M Financial services without them is not illustrate. Second, governance must be and build from the start, not added later. Third, start at the minimum viable platform. Focus on the first restriction areas, then expand. And fourth. Plan, the feature's edition will shape how Ready we are. Edge Auto ML and privacy reserving ml. Here's 90 Day Roadmap one. All ML workflows and complaints. Gap design requirements in both technical AGGREGATORY teams prototype the small team. It's usually have to do three points, deliver an MB, focus on high friction areas and measure success with KPAs like deployment time, donor efficiency, and level of satisfaction. So by calling these steps, organization can move from foundation to enterprise grade ML in a structured way. And that's it. I will bring this session to close. Thank you so much for your time.