AWS ML background

SageMaker was launched in November 2017 as an advanced ML service that could natively take advantage of cloud elasticity and scale. Analytics and ML are very challenging workloads because of how resource intensive they are. Analytics is a bit different because it focuses on drawing insights out of data from the past, whereas ML focuses on the future. Today, some ML platforms are either completely low code or, at least, easy to use. However, simplicity is often a trade-off for a low level of flexibility if your use cases do not fit into the existing patterns or algorithms.

SageMaker provides low-code features as well as a full-featured ML platform. SageMaker has automated ML (AutoML) low-code capabilities and it also provides the ability for you to package up your own custom models in a container and import them. Once the model is created, you can fine-tune it where necessary to meet your needs. One of the most impactful benefits is the ability to train models using...