Expanding Your Data Science and Machine Learning Capabilities

Surviving and thriving with data science and machine learning means not only having the right platforms, tools and skills, but identifying use cases and implementing processes that can deliver repeatable, scalable business value. The challenges are numerous, from selecting data sets and data platforms, to architecting and optimizing data pipelines, and model training and deployment. In response, new solutions have emerged to deliver key capabilities in areas including visualization, self-service and real-time analytics. Along with the rise of DataOps, greater collaboration and automation have been identified as key success factors.

Things we will discuss:

  • Moving from isolated pockets of success to an enterprise AI factory model where capabilities can be built and deployed consistently and repeatedly
  • Scaling enterprise machine learning across multiple dimensions, from strategy and people skills, to corporate compliance and technology platforms, to drive business adoption
  • Tapping technology platforms designed to unlock the power of open source for deploying machine learning capabilities with enterprise-grade security, governance and scale
  • Use cases and solutions for data science and machine learning spanning multiple industries, including companies such as Wish.com, China Mobile, Alipay/Alibaba, and Visa

To educate IT decision-makers and practitioners about new technologies and strategies for expanding data science and machine learning capabilitiesDatabase Trends and Applications is hosting a special roundtable webinar on April 30th.