If you’re looking for a drag-and-drop interface, first check Microsoft ML Studio. Please note that this overview isn’t intended to provide exhaustive instructions on when and how to use these platforms, but rather what to look for before you start reading through their documentation. Within this article, we’ll first give an overview of the main machine-learning-as-a-service platforms by Amazon, Google, Microsoft, and IBM, and will follow it by comparing machine learning APIs that these vendors support. Have a look at our data science team structures story to have a better idea of roles distribution or watch a video: These should be considered first if you assemble a homegrown data science team out of available software engineers. Prediction results can be bridged with your internal IT infrastructure through REST APIs.Īmazon Machine Learning services, Azure Machine Learning, Google AI Platform, and IBM Watson Machine Learning are four leading cloud MLaaS services that allow for fast model training and deployment. Machine learning as a service (MLaaS) is an umbrella definition of various cloud-based platforms that cover most infrastructure issues such as data pre-processing, model training, and model evaluation, with further prediction. Now let’s have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made. We’ve already discussed machine learning strategy. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers.īy using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. One of ML’s most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. And, if you’re aiming at building another Netflix recommendation system, it really is.
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