Home Artificial Intelligence (AI) Building the Optimal Machine Learning Platform

Building the Optimal Machine Learning Platform

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Building the Optimal Machine Learning Platform
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While various forms of machine learning have existed for several decades, the past few years of development have yielded some extraordinary progress in democratizing the capabilities and use cases for artificial intelligence in a wide multitude of industries. Image classification, voice recognition, fraud detection, medical diagnostics, and process automation are just a handful of the burgeoning use cases for machine learning that are reinventing the very world we live in.  This blog provides a brief overview of some of the basic principles of Machine Learning and describes the challenges and trade-offs involved in constructing the optimal Machine Learning platform for different use cases.

Neural Networks are key to machine learning

At the center of the growth in machine learning is a modeling technique referred to as neural networks (also known as deep neural networks, or deep learning), which is based on our understanding of how the human brain learns and processes information. Neural networks are not a new concept, and have been proposed as a model for computational learning since the 1940’s. What makes neural networks so attractive for machine learning is that they provide a mathematical ecosystem that allows the decision making accuracy of a computer to scale beyond explicit programming rules and, in a sense, learn from experience.

Previously, the limiting factor of neural network models has been that they are extremely computation intensive and require a tremendous amount of labeled data input to be able to “learn”. This double hurdle of processing power and available data had prevented them from becoming relevant…. until now.

Today, we stand at the intersection of huge data sets being generated in all corners of industry and the rise of massively-parallel compute infrastructure in the form of enhanced CPU instruction sets, GPUs, FPGAs, and new ASICs – designed specifically to accelerate neural network math. Neural network models that would have taken weeks or even months to run even a couple of years ago can learn (or be “trained”) in just a few hours on today’s hardware.

Read the entire article here, Building the Optimal Machine Learning Platform

Via the fine folks at Dell

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