Azure’s Cognitive Services provide prebuilt, pretrained models that support many common use cases, but many more need custom model development.
Going custom with ML
How do we go about building custom machine learning models? You can start at one end using statistical analysis languages like R to build and validate models, where you’ve already got a feel for the underlying structure of your data, or you can work with the linear algebra features of Python’s Anaconda suite. Similarly, tools such as PyTorch and TensorFlow can help construct more complex models, taking advantage of neural nets and deep learning while still integrating with familiar languages and platforms.
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