Look to the public cloud vendors not only to simplify big data infrastructure, but to make machine learning more accessible to mainstream developers by packaging functionality into easy-to-use API’s. For those enterprises that want to undertake strategic machine learning applications, partner with a professional services firm with the requisite skills, such as IBM, Accenture, or Palantir. Finally, put data feedback loops in place in order to keep improving the living models to ensure sustainable differentiation.
George has led conference panels with prominent thought leaders in cloud infrastructure and big data.He has been profiled on the front page of the Wall Street Journal and published as a guest author in a major overview of the evolution of cloud computingin The Economist.
Previously, George was the lead enterprise software analyst for Credit Suisse First Boston, one of the top investment banks serving the technology sector.Prior to being an analyst, George was a product manager on Notes at Lotus Development.
George received his BA in economics from Harvard University.
Latest posts by George Gilbert (see all)
- Recipe for Machine Learning Applications: Getting Started - 17 Jan 2017
- Machine Learning Pipeline: Chinese Menu of Building Blocks - 30 Nov 2016
- 2017 Big Data & Machine Learning Predictions - 30 Nov 2016