mazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow. First, you need to collect and prepare your training data to discover which elements of your data set are important. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. All of this takes a lot of specialized expertise, access to large amounts of compute and storage, and a lot of time to experiment and optimize every part of the process. In the end, it's not a surprise that the whole thing feels out of reach for most developers.
Amazon SageMaker removes the complexity that holds back developer success with each of these steps. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Amazon SageMaker significantly reduces the amount of time needed to train, tune, and deploy machine learning models. Amazon SageMaker manages and automates all the sophisticated training and tuning techniques so you can get models into production quickly.
Amazon SageMaker supports all machine algorithms and frameworks so you can use the technology you are already familiar with. Apache MXNet, TensorFlow, and Chainer are pre-installed, and Amazon SageMaker offers a range of built-in, high performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Amazon SageMaker lets you begin training your model with a single click in the console or with a simple API call. When the training is complete, and you’re ready to deploy your model, you can launch it with a single click in the
Amazon SageMaker is designed in three modules that can be used together or independently as part of any existing ML workflow you might already have in place.
Amazon SageMaker makes it easy to integrate machine learning models into your applications by providing an HTTPS endpoint that can be called from any application.
Amazon SageMaker is pre-configured with the latest versions of TensorFlow, Apache MXNet, and Chainer, with CUDA9 library support for maximum performance with NVIDIA GPUs. With Amazon SageMaker P3 instances running NVIDIA Volta V100 GPUs, Amazon SageMaker lets you train deep learning models with unparalleled speed.
Amazon Web Services (AWS) is a comprehensive, evolving cloud computing platform provided by Amazon.
AI is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
Big data is extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially relating to human behavior and interactions.
Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.