Fresh data beats stale data for machine learning applications. This on demand webinar discusses the value of fresh data as well as different types of architecture and challenges of online prediction, it will also cover the tradeoffs between latency, staleness, and cost.
As companies shift their Big Data to the cloud and hybrid environments, the need for Big Data analytics and a corresponding long-term analytics strategy has become increasingly critical. Here’s your opportunity to listen to experienced Big Data practitioners articulate their best practices in building successful, long term analytics architectures.
As data and analytics environments become increasingly complex, organizations can no longer afford to perform many operations manually. According to TDWI research, automation (in general) is one of the top three priorities for analytics. We see automation occurring throughout the data and analytics life cycle. Automation increasingly leverages embedded AI/ML algorithms (i.e., infused in the software) to help perform tasks such as profiling and cleansing data, identifying sensitive data, data mapping, surfacing insights, or building machine learning models.
In this webinar, Ramnath Vaidyanathan, VP Product Research at DataCamp, shares how DataCamp uses data in a big way to drive the development of products and content at scale. He gives some practical examples & insights of ways data has shaped DataCamp's products. Finally, Ramnath goes through the data-driven development process so you can apply the same principles to the products you are building.