Machine learning is all the rage. You take a software product, sprinkle some machine learning on it, and all of a sudden it’s supposed to be better, stronger, faster.
But what is machine learning, really? What’s it good for? Will it finally tell me the answer to Life, the Universe, and Everything? Or will it just lead to depressed androids?
The Datanauts starship has picked up a hitchhiker to help us level-set on this topic. He’s Ed Henry, Senior Machine Learning Engineer at Brocade Communications.
We start by attempting to separate fact from hype about machine learning. Then we talk about the algorithms that go into machine learning, IBM’s Watson, and how machines are trained on data sets.
We explore the infrastructure best suited to machine-learning workloads, including cloud vs. on-premises. And then we delve into use cases around image recognition, anomaly detection, and more.
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Amazon Machine Learning – Amazon
DataRobot Machine Learning – DataRobot
Google Cloud Prediction API – Google
IBM Watson – IBM
ImageNet – ImageNet.org
Pure Predictive – PurePredictive.com