Executive Briefing: A new taxonomy of machine learning
Rachel Silver shares a new taxonomy of machine learning approaches that distinguishes between those that are providing enormous competitive advantage and those that represent merely small, incremental improvements on existing analytical tools and details a framework for evaluating ML approaches on several dimensions of complexity, including:
The amount of data required (such as for training)
The computational complexity of the training algorithm
Real-time streaming requirements (versus just batch computing)
Data throughput for the deployed model to process
Rachel explores examples of how to apply this framework to real-world machine learning approaches and highlights the technical requirements of supporting the most disruptive examples of ML solutions.
The MapR Data Science Refinery container includes a FUSE-based MapR POSIX Client, optimized for containers, that allows deep learning libraries to read and write data directly to MapR-FS.
So, when you run TensorFlow, the compute occurs on the host where the container resides, but each container has full access to the persistent storage provided by the MapR Converged Data Platform. When you kill the container off, the data remains.
Complete Steps to deploy this can be found here.
Rachel Silver is the Product Management Lead for Machine Learning & AI @ MapR Data Technologies.