In this document, the Standard Methodology for Analytical Models (SMAM) is described. A short overview of the SMAM phases can be found in Table 1. The most frequent used methodology is the Cross Industrial Standard Processes for Data Mining (CRISP-DM), which has several shortcomings that translate into frequent friction points with the business when practitioners start building analytical models.
A new curated list of medical data for machine learning is available here.
In addition, Stanford is developing a petabyte-scale, cloud-based, multi-institutional, searchable, open repository of diagnostic imaging studies for developing intelligent image analysis systems - called Medical Image Net.
These curated data sets are great for experimenting - please respect data usage restrictions for each data set.
Today data scientists are using new techniques with machine learning to solve complex challenges. In applied data science speed kills and the Tesla V100 accelerator is built for speed. Data scientists always make trade-offs between accuracy and time. Thus, more powerful compute systems are required to crunch and analyze diverse data sets, and train exponentially more complex deep learning models in a practical amount of time.
MapD Core is an in-memory, column store, SQL relational database designed to run on GPUs (also runs on CPUs). It is now available free - licensed under the Apache License, Version 2.0.
Within a single server, MapD can handle datasets 1.5-3TB in raw size in GPU RAM, or 10-15TB in CPU RAM. In distributed deployments, that number scales linearly.
Qualcomm's new Snapdragon 820 chip (used in many high-end smartphones) can be used for deep learning and neural network applications. The Snapdragon Neural Processing Engine is an SDK powered by the Zeroth Machine Intelligence Platform.