Amazon SageMaker: Infinitely Scalable Machine Learning Algorithms At AWS, we continue to strive to enable builders to build cutting-edge technologies faster in a secure, reliable, and scalable fashion. Machine learning is one such transformational technology that is top of mind not only for CIOs and CEOs but also developers and data scientists. November 2017, we launched Amazon SageMaker to make the problem of authoring, training, and hosting ML models easier, faster, and more reliable. Now, thousands of customers are using Amazon SageMaker and building ML models on top of their data lakes in AWS. While building Amazon SageMaker and applying it to large-scale machine learning problems, we realized that scalability is a key aspect we need to focus on. So, when designing Amazon SageMaker we took on a challenge: to build machine learning algorithms that can handle an infinite amount of data. There are many other challenges. For example, Machine learning models are often trained tens or hundreds of times. During development, many different versions of the eventual training job are run. Then, to choose the best hyperparameters, many training jobs are run simultaneously with slightly different configurations. Finally, re-training is performed every x-many minutes/hours/days to keep the models updated with new data. Training, therefore, must be both fast and cost-effective. To that end, Amazon SageMaker offers machine learning algorithms that train on indistinguishable-from-infinite amounts of data both quickly and cheaply. This sounds like a pipe dream. Nevertheless, this is exactly what we set out to do. This talk lifts the veil on some of the scientific, system design, and engineering decisions we made along the way.