Abstract: Analytics in the cloud is becoming increasingly popular among organizations, and this trend is expected to continue in the future. Businesses want to avoid the expense, complexity and expertise required when managing analytical workloads using in-house infrastructure. Different analytics consumption models, powered by the cloud, provide businesses with greater flexibility and scalability, allowing them to focus on solving specific use-cases and scale up or down as needed. However, the cloud introduced new complexities, resulting in the repatriation of analytical workloads back on premise by many organizations. Among the main reasons were performance, cost and control, due to the unexpected behavior that was observed in those areas. These issues come to add to the long list of challenges that organizations are facing in data science today in terms of talent shortage, employee churn and lack of productivity.
In the cloud computing world, performance and productivity are closely interlinked with cost control, revenue and time to value, which are all essential elements for organizations to maintain their competitive edge. During this session, we will discuss the enablers that organizations need to unlock productivity with analytics and the importance of optimized algorithmic performance in the cloud to reduce costs, so organizations can derive maximum value from their investments. We’ll also touch upon the ideal state of cloud performance for analytical workloads and present scenarios that showcase how different implementations of algorithms behave in terms of runtimes and CPU usage when we add computing resources in the cloud. Finally, we’ll share best practices to keep cloud costs in check and ensure a smooth digital transformation journey when moving analytical workloads to the cloud.
Bio: Spiros Potamitis is a data scientist and a global product marketing manager of forecasting and optimization at SAS. He has extensive experience in the development and implementation of advanced analytics solutions across different industries and provides subject matter expertise in the areas of forecasting, machine learning and AI. Prior to joining SAS, Spiros worked and led advanced analytics teams in various sectors such as credit risk, customer insights and CRM.