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. Image and (no) video processing APIs: IBM Visual Recognitionįor most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding.Image and video processing APIs: Google Cloud Services/ Cloud AutoML.Image and video processing APIs: Microsoft Azure Cognitive Services.Image and video processing APIs: Amazon Rekognition.Speech and text processing APIs: IBM Watson.Speech and text processing APIs: Google Cloud ML Services/ Cloud AutoML.Speech and text processing APIs: Microsoft Azure Cognitive Services.Speech and text processing APIs: Amazon.Machine learning APIs from Amazon, Microsoft, Google, and IBM comparison.
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