Amazon SageMaker Xiaolong(Marlon) Ma Introduction ● Amazon SageMaker is a cloud machine-learning platform enables developers to build, train, and deploy machine-learning (ML) models in the cloud. ● Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost. Why SageMaker ● Build and train your machine learning model on the cloud ● Distributed machine learning becoming popular ● End-to-end machine learning solution Sagemaker Architecture Build ● Collect & prepare training data Data labeling & pre-built notebooks for common problems ● Choose & optimize your ML algorithm Built-in, high-performance algorithms and hundreds of ready to use algorithms in AWS Marketplace Collect and prepare training data ● Amazon SageMaker Ground Truth helps you build and manage highly accurate training datasets quickly. Ground Truth offers easy access to public and private human labelers and provides them with pre-built workflows and interfaces for common labeling tasks ● Hosted notebooks Fully-managed Jupyter notebooks that you can use in the cloud or bring notebooks from your local environment to explore and visualize your data and develop your model. Choose and optimize your machine learning algorithm ● Amazon SageMaker automatically configures and optimizes TensorFlow, Apache MXNet, PyTorch, Chainer, Scikit-learn, SparkML, Horovod, Keras, and Gluon. ● Commonly used machine learning algorithms are built-in and tuned for scale, speed, and accuracy with over 200 additional pre-trained models and algorithms available in AWS Marketplace. ● You can also bring any other algorithm or framework by building it into a Docker container. Train ● Set up & manage environments for training One-click training using Amazon EC2 On-Demand or Spot instances ● Train & tune model Train once, run anywhere & model optimization Train ● Automatically tune your model Automatic Model Tuning uses machine learning to quickly tune your model to be as accurate as possible. ● Train once, run anywhere Amazon SageMaker Neo lets you train a model once, and deploy it anywhere. Deploy ● Deploy model in production One-click deployment ● Scale & manage the production environment Fully managed with auto-scaling for 75% less Deploy ● Elastic Inference Reduce your deep learning inference costs by up to 75% using Amazon Elastic Inference to attach elastic GPU acceleration to your Amazon SageMaker instances easily. ● EC2 Instances Auto Scaling Amazon SageMaker manages your production compute infrastructure on your behalf to perform health checks, apply security patches, and conduct other routine maintenance, all with built-in Amazon CloudWatch monitoring and logging. Questions.