BUILDING A MARKETING DATA SCIENCE CLOUD WITH HORTONWORKS

Data, the life blood of today's economy, is an attractive target for cyber attackers. Learn how a self-securing database provides security and peace of mind without relying on manual, error-prone processes allowing you to focus on innovation and not damage control.

Why is it that 80% of enterprises fail to scale AI? Data scientists face operational, collaborative and infrastructure complexities at each step of the ML lifecycle. MLOps practices have the ability to solve many ML operational concerns such as project deployment, testing, serving and monitoring. In this webinar, Yochay Ettun, CEO and Co-founder of cnvrg.io will discuss the ways that MLOps solutions empower data scientists to successfully operationalize ML by applying DevOps principles to the ML lifecycle.

Modern data applications and analytics rely on a wide variety of data both inside and outside the company; organizations depend on enriched data sets for better insights. This need, in part, has driven many companies to move to cloud data warehouses and cloud data lakes. However, it’s no longer simply about migrating to the cloud. It’s about modernizing using a combination of industry-leading services in the cloud and cloud-native data management services to deliver better business decisions, faster.

Organizations that operate worldwide typically need to manage data both locally and globally. Local business units and subsidiaries must address region-specific data and accounting standards, regulations, customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the entire enterprise. For many global firms, data is the business. They need state-of-the-art data management just to remain innovative and competitive. Hence, multinational businesses face a long list of new business and technical requirements for modern data management.