This is the first of a two-part post on the current state of advanced analytics in Hadoop. In this post, I’ll cover some definitions, the business logic of advanced analytics in Hadoop, and summarize the current state of Mahout. In a second post, I’ll cover some alternatives to Mahout, currently available and in the pipeline. For starters, a few definitions.
Read morePart four in a four-part series. We’ve mentioned some of the technical innovations that support an Agile approach to analytics; there are also business practices to consider. Some practices in Agile software development apply equally well to analytics as any other project, including the need for a sustainable development pace; close collaboration; face-to-face conversation; motivated and trustworthy contributors, and continuous
Read moreThis post is the second in a four-part series. Agile Analytics is an approach to predictive analytics that emphasizes: Client satisfaction through rapid delivery of usable predictions Focus on model performance when deployed “in market” Iterative and evolutionary approach to model development Rapid cycle time through radical reduction in time to deployment The Agile approach focuses on the client’s end
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ML/AI 