Empowering Finance with AI/ML, Edge, and Kubernetes Revolutionizing the Finance Industry through AI, ML, and Edge Computing with Rancher-managed Kubernetes openSUSE Conference 2023 [email protected] oSC23 @openSUSE Presented by: Navin Chandra 

About the speaker Navin Chandra • From New Delhi, India • 2nd Year Computer Science Student • Google Summer of Code Contributor at Rancher, openSUSE • Promotes open-source software and technologies 

Overview of AI and ML in Finance AI and ML technologies have revolutionized the finance industry, enabling advanced analytics, automation, and decision-making. In banking, AI and ML are used for customer service chatbots, fraud detection, and personalized recommendations. In finance, AI and ML algorithms power High-Frequency Trading (HFT) strategies for quick decision-making based on real-time market data. 

Importance of Edge Computing • Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. • Edge computing plays a crucial role in finance by bringing processing closer to data sources, reducing latency, and improving response times. 

Use-cases of AI/ML at the Edge in Finance • With edge computing, financial institutions can make real-time decisions, monitor transactions, and quickly identify anomalies or potential fraud. • Some of the edge use cases are: • Real-time facial recognition for fraud detection and prevention. • High-frequency algorithmic trading (HFT). 

The Role of Kubernetes and Containerization • Kubernetes: Vital for scalable AI/ML deployment at the edge. • Containerization: Enables efficient, portable deployment across edge environments. • Kubernetes: Simplifies container management, automates scaling, optimizes resources for seamless AI/ML operation at the edge. 

Advantages of Rancher for Kubernetes Deployment and Management • Rancher: Streamlines Kubernetes cluster deployment, operation, and scaling. • User-friendly interface: Simplifies management and monitoring of finance- focused AI/ML deployments. • Rancher: Enables efficient management of Kubernetes across distributed edge environments. • Rancher Apps/Extensions: Integrate third party apps like Prometheus, Grafana, etc. from Rancher with ease • K3s: Small and efficient k8s distribution tailor made for edge environments. 

Integration of AI and ML at the Edge with Kubernetes and Rancher • The combination of AI/ML at the edge with Kubernetes and Rancher empowers the finance sector with advanced capabilities. It enables efficient deployment and scaling of AI/ML models at the edge, leveraging real-time processing and low latency for improved decision-making. • Kubernetes and Rancher provide a reliable and manageable infrastructure, addressing challenges in implementing AI/ML at the edge, and facilitating cutting- edge applications in the financial industry. 

Example deployment of an ML stock forecasting app on the edge using K3s and Rancher. 

Explore the previous project and find out how you can deploy AI/ML apps on Kubernetes with Rancher: • A step-by-step technical reference documentation to deploy this ML app with Rancher, scan this QR code to read and follow through it. 

Thank You Please feel free to ask any questions. 😄