Hi everyone. Thank you for joining. My name is, and today I will walk you through the ML ops in healthcare, or simply how we can scale AI models in hospitals and clinics by using smart automation and monitoring frameworks. Before we dive in, I have a quick question. How many of you have seen an AI project in healthcare that worked great in testing, but struggle to go live? I'm pretty sure a lot of y'all have seen that. Yes, that's exactly the gap we are addressing today. Let's move on to the next slide, the healthcare ML ops challenge. Healthcare organizations are excited about ai. It promises better diagnostics, faster admin processes, and even cost savings. But here is the reality. 68% of projects face IES production failures reduce trust and compliance slows everything down. So let me ask you, what do you think is harder building the AI model? Or getting it safely into production. Most of you would agree. Deployment is where things get tough. Let's move to slide three, research backed lops Impact. Now, here is the good news. Research shows that structure lops makes a huge difference. Projects succeed. 73% more often, models stabilize 40% faster errors reduce, and organizations save millions each year. So if I could summarize, ML Ops isn't just about better tech, it's about better outcomes, faster and safer. Let's move on to the next slide. Healthcare specific ML lops challenges. But healthcare is different from other industries. We face four unique hurdles. First is regulation, meeting FDAs and HIPAA standards. Second is sensitive data. Patient privacy is very critical. Third is workflow fit. Doctors cannot be slowed down by clunky systems. For is monitoring at scale across hospitals. Not just one system. Now the question is, do any of these resonate with challenges you have seen in your own organization? Something to think about. Alright, let's move to the next slide. Comprehensive framework. To solve this, I use a four phase framework model assessment, infrastructure readiness, pipeline automation, monitoring systems. Think of it like building a hospital. First, you can check the quality of materials. That is the model assessment. Then you make sure that the building is safe and compliant. That is the infrastructure. Next, you automate daily operations like elevators and security. That is the pipelines. Finally, you keep everything under 24 7 surveillance. That is the monitor. Let's move to the next slide, phase one. That is the model assessment. Healthcare models can't just be accurate. We need to check, does it work across different patient groups? Is it fair and unbiased? Can clinicians understand its decisions and can we prove this to the regulators when we follow this process accurate. Rates go up by 64%. So assessment is our foundation. Moving to the phase two infrastructure readiness, imagine trying to run MRI scans on a laptop. It just won't work. Infrastructure readiness means making sure we have the right compute and security, ensuring HIPAA compliant flows. Designing integrations with systems like EHR, so doctors aren't disrupted, and planning for scalability when more hospitals adopt the model. Doing this right reduces disruptions by 68%. Let's move to the next slide, the phase three, which is automated pipelines. This is where we save time and avoid errors. Pipelines take care of data, pre-processing and de-identification. Moderate training with version control. Safe deployments, using cannery tests and rollbacks automatic generation of documents for regulators. Here is a quick analogy. It's like setting up an assembly line in a factory. Once built, it runs smoothly and consistently. Let's move to the next phase. That is a phase four monitoring framework. Now, here is the most critical part. That is monitoring. We track model accuracy in real time data drift when patient data starts changing. Alerts for clinicians. If something looks wrong, this step improves reliability by 47%, and most importantly, keeps patients safe. Let's move to the next slide. Technical implementation on ml Lops Architecture. For those of you who like the tech site here is the stack version Control with Git, CICD, pipelines for approvals, Dockers and Kubernetes for deployment, Prometheus and Grafana for monitoring. But remember, technology, just the enabler. The real goal is trust and safety in clinical use. Let's move to the 11 slide. That is the implementation roadmap. Rolling this out takes about a year, but we break it into phases. It starts with month one and two where we assess and plan. Month three and four, where we build the foundation. Month five to six, we pilot with one model. From month seven to 12, we scale to more hospitals. Question is, if you had to pick, would you prefer a faster pilot or a more cautious rollout across the system? This is something which we need to think about. So let's move to the key takeaways. So to wrap up our session today, we have some key takeaways. Structure lops is equal to higher success rates. Healthcare has unique challenges, but they can be sought. The four phase framework gives us a practical path, and the ROI is clear, safer, faster, and more cost effective deployments. The next step, starting by assessing where your current MLO system starts. Thank you so much for your time and your engagement today. I would love to hear your thoughts about what is the biggest deployment challenge you're facing.