Hi, welcome everyone to the Reliability Engineering 2025 conference. I'm Bawan Carta. I'm a specialist leader at Deloitte's AI and cloud engineering practice. Today I will be talking about the interesting topic of integrating AI into cloud platforms and provide a roadmap for enterprises. Looking to undertake this journey. So we'll start with a quick introduction into these topics. So the fusion of artificial intelligence and cloud computing is reshaping the enterprise landscape and offering unprecedented opportunities for innovation efficiency. And, competitive advantage. So far, businesses this is no longer just a optional transformation, but it's more of a strategic initiative. However, the journey of integrating these two technologies is is complex and has its own challenges. So it requires careful planning robust architectures. Also a clear understanding of operational challenges. So this talk aims to provide a detailed roadmap for enterprises that are aiming to integrate AI into their cloud ecosystems. Next, we'll move on to the next slide. So why does AI and cloud integration matter? The synergy between AI and cloud computing is a game changer for enterprises. Cloud platforms provide the scalable infrastructure needed to train and deploy AI models. While AI enhances the cloud capabilities with intelligent automation predictive analytics, and real time decision making. So together these two technologies can enable organizations to accelerate their innovation cycle optimize operational efficiency as well as gain actionable insights from vast data sets. However this process is not without its challenges. There are a lot of issues related to data governance, model management, and talent shortages that need to be addressed before we can take advantage of the integration of these two technologies, please. So next we will look into why building the right architecture. Is key to enabling this integration. So many organizations operate in hybrid or multi-cloud environments to avoid vendor lock-in and to optimize costs. Integrating AI into these setups requires a unified architecture that ensures seamless data flow and model deployment. Using strategies such as, open standards Kubernetes for interoperability as well as implementing a data fabric to enable consistent data access. And this combined with emerging technologies such as edge computing, serverless computing these are going to enable organizations to have the foundational framework and the architecture required to support this integration. Also importantly, AI integration must align with the enterprise business objectives. A use case driven approach ensures that these AI initiatives are not just technology playbooks, but that they deliver tangible value as well. Next we will move on to look at what are the key challenges. In that we see in this journey and how to overcome them. So AI models typically rely on high quality data. So enterprises must establish robust data governance to ensure data privacy, but also to critical things such as security, compliance, et cetera. So key strategies here include implementing data lineage. And using automated tools for data cleaning and pre-processing, et cetera. And on top of that it's also critical to have lifecycle management capabilities for all of the machine learning operational readiness, right? This includes things such as version control, CICD pipelines and real time monitoring of the performance of these models and keeping track of them to enhance them further. Last, but not the least is the talent strategy. Enterprises must invest in upskilling existing employees and attracting specialized talent to support AI and cloud integration initiatives to bridge the skill gap that currently exists. So now that we spoke about the foundational components. Next we'll look at a step-by-step roadmap for enterprises looking to undertake the cloud AI integration journey. So this is divided into a logical phases four phases that will help organizations reach their transformation goals in a phased manner. Phase one here we start with, building the foundations. These include things such as assessing the AI maturity and identifying like the high impact use cases, establishing data governance that we discussed previously and also, selecting the cloud platforms and vendors that align with the enterprise goals and with those foundational capabilities. Next you can move on to the pilot deployment, which would include, development and deployment of the pilot AI models for selected use cases. And then on top of that, implementing the ML ops pipelines and practices for model management. And then also being able to evaluate the model and fine tune the model for performance as needed. Once the pilot deployment is successful, then organizations can look into scaling and optimization. This would involve scaling successful pilots across different line of businesses within the enterprise, optimizing the architecture for cost, performance, scalability, et cetera. And also integrating emerging technologies like edge computing, serverless and so on. And the last phase would involve continuous improvement. This is where the scaled model has to be monitored and all the KPIs track diligently. So that, any process improvements, any technology related improvements can be monitored and can be incorporated facilitating continuous improvement into the whole framework. Now that we've looked at how organizations can take a phase approach to reach their transformation goals in terms of AI and cloud integration. Now we'll quickly look at what the future holds for this area. So the integration of AI and cloud integration is very much a evolving field with several emerging trends that are set to shape its future. Ai cloud cloud management using AI tools which uses AI to optimize cloud resource allocation. Then we have, federated learning which.