Building fully custom machine learning models on AWS SageMaker: a practical guide

AWS SageMaker is a cloud machine learning SDK designed for speed of iteration, and it's one of the fastest-growing toys in the Amazon AWS ecosystem. Since launching in late 2017 SageMaker's growth has been remarkable -- last year's AWS re:Invent stated that there are now over 10,000 companies using SageMaker to standardize their machine learning processes. SageMaker allows you to to use a Jupyter notebook interface to launch and tear down machine learning processes in handfuls of lines of Python code, something that makes data scientists happy because it abstracts away many of the messy infrastructural details to training. The thesis: standing up your own machine learning algorithm should always be this easy! SageMaker has two APIs: a high-level API for working with a variety of pre-optimized machine learning libraries (like MXNet, TensorFlow, and scikit-learn), and a low-level API that allows running completely custom jobs where anything goes.