Hello everyone. I'm Teta with about 15 years of experience into integration and AI and enterprise integration platforms. So today we are exploring how machine learning acts as a catalyst for cloud native digital transformation over the next several flights. We will see how embedding ml into microservices containers and serverless architectures not only accelerates innovation, but also drives operational efficiencies that weren't possible before. So before diving into the topic, let's just cover the evolution of cloud architecture. Let's begin with cloud architecture. We started with traditional monolithic applications, static, tightly coupled and hard to scale systems. Then came lift and shift migrations to the cloud where we gained better sources utilization, but little architectural change. Cloud native approaches broke these monolithics into microservices in containers with orchestration delivering resilience and flexibility. So finally, by augmenting this model with embedded ml, our systems continuously learn and adapt unblocking predictive and automated decision making. In the next slide let's just look about the distributed computing benefits. Deploying ML in a cloud native environment lets us leverage distributed computing to its fullest, VHU around 10 to 20 times faster training via paralyzed pipelines. Elastic scaling automatically matches resources to demand. So a petabyte scale data processing becomes routine and we only pay for what we use. So we would be optimizing costs. So together these capabilities enable us to tackle complex problems at unprecedented scale. Speed. So now let's look about the intelligent automation capabilities. So ML driven cloud integration powers intelligent automation across it operations With predictive scaling systems can anticipate spikes before they occur. So self-op optimization tunes parameters continuously based on performance data and automated security detects and neutralizes threats without manual intervention. So organizing organizations leveraging these features report around 45 to 60% of less downtime and 30 to 40% of greater resource efficiency. Now let's look at the cross cloud mul machine learning deployment. So in hybrid and multi-cloud world, we need seamless ml across environments. So centralized governance ensures consistent model management, monitoring and security policies everywhere. Edge deployments push inference closer to latency sensitive users. Provider optimization lets us exploit hardware accelerators or pricing advantages and failover, recie keep predictions services running even when a region goes offline. So this flexibility strategy maximizes performance. While guarding continuity. Now in the next slide, we will be looking at the implementation challenges. In the implementation challenges in today's integrating ML into a cloud native systems is not without hurdles. So models can drift as real world data changes, demanding continuous monitoring and retraining. Deep Networks act as black boxes making inter interpretability and audit auditability difficult. So ensuring high quality data across distributed services remains a perennial struggle and new security risks advisor attacks, model poisoning. Privacy leaks require robust defenses. So overcoming these challenges for structured processes and specialized skillsets. Now let's look at what are the emerging solutions for this integrating ML into cloud native systems. So emerging, the ecosystem is evolving rapidly, so ML ops platforms automate the end-to-end lifecycle. From data prep through deployment, slashing deployment times by almost 80% and enforcing reproducibility. So ex explainable AI frameworks like SHAP and LIME, demystify model behavior to build trust and meet compliance requirements. Federated learning lets us train on decentralized data without ever moving it. Preserving privacy and cutting bandwidth costs. These innovations make cloud native ML far more accessible. So now let's look about the business impact metrics. When we measure the outcomes, the gains are striking. Teams deploying ML powered cloud solutions bring new features to market 65% faster. So operational efficiency leaps forward. So customer satisfaction and employee productivity both ways. And while revenue growth improvements may appear modest in percentage terms, they translate into significant absolute gains. So leaders report around 20 to 30, 20 to 30% higher growth versus pace relying on the conventional architecture. So now let's look at, look, let's look about the implementation framework for this ml integration. So a proven roadmap ensures these benefits materialize. First, conduct a strategic assessment to align ML initiatives with business priorities Next. Design your cloud native ML architecture defining microservices, data pipelines and governance. Then run a focused POC or proof of concept to validate assumptions and demonstrate clear RYA, written on investment. Finally, scaling successful patterns enterprise wide building the organizational confidence and operational rigor you need for long-term success. So what are the future trends in this article in this area? What's on the horizon? Autonomous ML promises self designing neural architectures that minimize human effort in model creation. AI specific hardware think AI optimize. The processors in the cloud will deliver order of magnitude performance boosts. So mesh intelligence will embed, distribute distributed ML throughout and organization ecosystem, enabling services to share insights seamlessly and natural interfaces conversational and multimodal will put ML capabilities directly into the hands of business users. Without running requiring specialized technical skills. So what are the key takeaways and the action plan in this session? So to wrap up four priorities will set you up for success. First, align each ML initiative with clear business value and metrics. Second ensure your platform is truly cloud native containers microservices. Infrastructure as code before you layer on the ml. Third, invest in DataOps and ML ops to automate the pipelines, maintain model quality at scale. And lastly, upskill your team upskilling combining data science cloud, cloud engineering, and domain expertise in cross-functional squads. So following this action plan, and you will build adaptive intelligent systems. That continuously drive business values. And lastly, thank you everyone for your time and detection and how I hope this discussion has given you a clear view of how ML and Cloud Native patterns coverage to accelerate digital transformation. I'm happy to take any questions or dive deeper into any of these topics. Thank you.