Sprint or Marathon? Going the distance with data center maintenance

As organisations look to enrich their approach to advanced analytics, employees company-wide are filling the skills gap. These are todays citizen data scientists, dedicated to solving problems and building solutions with data, no matter what their background. For them to truly exceed, they need powerful, enabling technology and a framework to use it.

• The top ten strategic data and analytics technology trends and what they enable. How these trends enable you to build an intelligent and emergent data and analytics portfolio of capabilities that scale to the needs of digital business. Why these trends are growing and having an impact now. How these trends will change your organization, data and analytics program and skills needed. Strategic technology trends have significant disruptive potential over the next 5 years. You must examine your business impacts of these trends and appropriately adjust investments, business models and operations or else your company is at risk of losing competitive advantage to those who do. Data and analytics leaders cannot afford to ignore these 2019 top data and analytics trends.

This session will discuss how the master data management platforms are evolving to meet needs of digital economy. A modern master data management platform incorporates graph technology, infuses insights from the data using advanced analytics and ML, and offer big data scale performance in the cloud. Join this webinar to learn about these and other critical capabilities that power connected customer experience, compliance, and business alignment.

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.