The pace of business disruption is accelerating, meaning today’s organizations need to become more data agile in order to compete and innovate. It’s no secret that big data initiatives are becoming more pervasive. But in order to process this vast amount of data, companies need data science and machine learning to find valuable insights. As they move to build smart applications powered by big data and IoT, new challenges are arising including how to move data science and machine learning into production. Oftentimes it is a laborious, manual-coding process that can take up weeks or months.
In this session we’ll discuss how you can: • Pre-process data in order to train data models • Operationalize data science and advanced analytics • Embed machine learning into real-time big data projects to accelerate time-to-insight
