Hey everyone, this is Dlip Kumar and I'm thrilled to be here today. I'm currently working as advanced cloud engineering professional with over 19 years of experience in the information technology industry. Over the past several years, I have been deeply engaged in initiatives that leverage artificial intelligence and machine learning to solve critical business challenges, and particularly within the financial services domain. My experience with Swift integration onto Azure has provided unique insights into how machine learning can enhance fraud detection and risk management In payment processing, I have actively explored how to apply machine learning models to improve security and efficiency of financial transactions, optimized resource allocation, and improve predictive modeling for customer support. Today we are here to explore how we ensure these models can operate effectively in the real time world. I will demonstrate how ML Ops can help scale the models for financial systems and walk through the key components of successful ML ops strategy for financial institutions. So we'll try to explore on understanding the problems and identify effective solutions that is crucial for achieving success. Ultimately, our goal is to ensure success in that. Financial industry so that fewer individuals suffer from fraud. We'll provide guidance on how to implement the steps needed for AI models to be reliable and capable of scaling effectively with the financial sector. Through this mops, principles can step in, techniques, can help in creating robust and highly effective ML pipelines. Okay, so how the current state of ml in financial services looks like. So financial institutions have increasingly realized that the machine learning isn't just a passing trend, it's truly changes the landscape, right? So they are increasingly relying on ML for the first application is a real world. You, if you can consider that as a realtime fraud detection, allowing us to picture the potential prevention of a fraudulent transaction. So the models can catch the crime instantly, avoid the loss for all who are, who all involved. So this will also contribute to make an easier financial system for every single one. So additionally ML can make accurate credit risk assessments and follow specific financial regulations. However lots of organizations experiencing the complications in implementing the above. What are all the things that are there? So usually we will not be able to experience such kind of complications when you try to follow the process or the governance framework around it. Putting models in place is often done manually, like imagining a team, taking a weeks to get set up, putting systems in place, and also installing code rather than focusing on making their models better. So the poor tracking of models can cause them to shift over time, losing their accuracy. Governance gaps, create complaints, problems for all of those about things, right? We will discuss how ML Ops has solved all those issues, right? So with that, so we can see how ML Lops transformation opportunity is going to be looking like for us, right? So let's take a look at what ML Ops really is. So in short, mops are machine learning operations. We can say like it's the same as how you call it as DevOps development operations, right? But for ML ops in, as it has a foster deployment to speed up models. So deployment of systems can occur with just hours which has never happened before due to the old method old methodology of implementations. So now lops can create an environmental of enhancement, reliability, therefore preventing of any issues. So good collaboration with other engineers is very important, and lops can provide just that. So in addition to the various other great points the one thing to highlight is the ML ops enhances regularity, adherence, right? So all of these things that ML Lops provide can help create a strong foundation for the AI models to develop. So with these concepts and more, an organization can scale with agile management and be set up for a very nice system. So to help out all these kind of implementation of ml. So with these things like you can really achieve what we are actually looking for. So the core, core components of financial ML lops think of these core parts at the blueprint for building a successful ML lops framework. So they are the main building blocks that connect to make a great and robust system. So infrastructure automation is the key point here. These are the clouds to manage resources and set up with infrastructure as a code. So the whole system must be defined as a code. So ci cd pipeline automate the process of building models. Testing and deployment, all the actions can be quickly done and automated. Monitoring and alerting is the place where everything is checked. Always check if something is wrong or not which is always a good thing, right? Like whenever something is wrong, like you immediately alert it. So since that can cause if you handle the alert well, it can significantly avoid any issues to happen. Governance framework makes sure everything goes as per the plan. In a regular regulatory compliance point of view, right? It's important to take a note in governance. So these are all the, these are all for major for implementing a financial ML strategy. So infrastructure as a code for ML neuro environment. So it is extremely important to understand how necessary infrastructure as a code is in the financial sector, right? So where regulations and safety and security are very much important in this financial institutions especially. So if you, if there is no security, there is so much of fraud to be prone in those systems to be. So that's why like you have. We should really be focusing on isolated development environment so that there are no problems with current or new projects. It's extremely important to test every single step and also the complete project you have to review thoroughly. So there must be some sort of production infrastructure so that deployment is safe and secure. So with the systems like Terraform, it makes setting up and running the systems easy. You can also set your own security in place with these systems. So how CI ICD pipelines for model deployment it's going to be look like, right? So the ci cd pipelines are like an assembly line for artificial intelligence. So this means every step can move smoothly without breaking. So CI I CD will help automate systems with the steps like automated testing and checking for a deployment. And how the post validations are happening. Is there any pre-steps that needs to be implemented before the deployment? And all these things can come into, continuous integration and continuous deployment lifecycle. So you can test as many as areas using automated testing. This makes it more accessible to detect fraud and bad actors, especially, right? So this way you can easily handle CIC. When it comes to advanced monitoring for financial ML models, so data accuracy must be present for the models will lose the edge. So the way to solve this is having a constant lance and monitoring the data all the time. So another thing is model metrics needs to be tested constant, constantly as well, right? So this involves running through different steps and setting outcomes become the model. So this constant cycle makes the model accurate and ready to be useful for the users to test it out. So the last thing to do important can start tracking to make sure the data does not shift and change. So this can be solved through checking feature importance. All of these helps improve a model performance. So how automated model model retaining workflows that, we can imagine in this particular scenario, like you have to make sure the models don't lose relevance. So keep in mind that the models have to keep up with every change that is out there to prevent this. Automate models are there to help out with every situation, key components or drift detection to see if the data changes, right? So if the situation is a change in data. Then next steps is to update through the training pipeline. After this is done, then everything is done and the model should be ready for you to use for a real world scenario. Any example, all of this happens through an automated components. With all of this in mind, leading firms can de, deploy models into just hours versus it becoming take days and deployment like so everything comes down to a needing a quick setup because all these settings can be very difficult and slow. So how ML governance and compliance can be implemented for compliance models have to go through the same rigorous steps. The step. The first step is to take when setting up governance is the documentation you need to document, model data, and steps to make sure nothing bad will happen. With all those in mind, you can follow and automate systems when that occurs. Compliance with government can also occur in just more natural. So cloud platform and implementation patterns we can discuss, like to create a model that cloud is very much, IM a factor nowadays because everything is, cloud version of data being a Azure platform or Google cloud platform or AWS like whatnot. Like you can consider any cloud, but it lets you, create your own artificial in inte platform and have what it needs to be successful platforms can do what they do best, right? However, you must consider all situations before making a choice. All in all, it comes down to your needs and situations to do customizing systems to make them unique and in sync with important great artificial intelligence solutions. So how real time inter like scaling for financial systems. So in the space of financial systems like every second matters, as one of the things that everyone expects are 24 7 availability and zero downtime for situations of fraud. If one second there is a fraud attack, there should be always be severe, ready to deal with these kind of issues immediately. One of the strategies to do this is to have servers that automatically adjust. So that allows them to handle any type of, volatility, right? So you have to be ready for any type situation. AB testing frameworks for model validation. How you can do it before every launch, right? Like you always wanted to test if it'll work. The method you can implement is using AB testing. Ab, it makes sure that each change goes smoothly. You also need to make sure that they are not destructive and that they are very much safer to do and when it comes to automated bias detection and mitigation like in every system people come from a different background, ages, and more. You cannot discriminate against anyone, right? So it should be fair for in every AI step that you are gonna implement. So implement the right process that we'll catch. If AI is not what as expected, so cross-functional ML ops team structure, how it's going to be. So for artificial intelligence relative, you need to a solid team in order to prevent this. Everyone should work together. So there should not be any team that, that say we don't, we do not connect. So we have to be very very manageable at times. Like with all the cross-functional ML ops team structures, everything has to connect seamlessly so everyone can work their best and give the best results. So key and next steps. In summary, there is a lot a lot that goes into the system, right? As you all know, like you have to monitor, automate, and be secure. Everything has to work together. But the highest chance of success, if you are starting up, one of the main things you should do is measure the situations. To do this, you should measure what is there, like what needs to improvement, and then you are set to go. These are all the main components that we can consider. This is what I can, I know I can provide with my best experience. So with all the information that I have had by working with closely with, many teams, many people in the past. So it might be a useful session for you folks, like hopefully it's giving you good enough content. Thank you all.