Amazon SageMaker
Machine learning for every developer and data scientist
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.
Build machine learning models
Reduce data labeling costs by up to 70% using Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth helps you build and manage highly accurate training datasets quickly. Ground Truth offers easy access to labelers through Amazon Mechanical Turk and provides them with pre-built workflows and interfaces for common labeling tasks. You can also use your own labelers or use vendors recommended by Amazon through AWS Marketplace. Additionally, Ground Truth continuously learns from labels done by humans to make high quality, automatic annotations to significantly lower labeling costs.
Build models using a single web interface
Amazon SageMaker makes it easy to build machine learning (ML) models at scale and get them ready for training, by providing everything you need to access and share notebooks, and use built-in algorithms and frameworks. Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place. You can use Amazon SageMaker Autopilot together with SageMaker Studio to automatically generate models. SageMaker Autopilot is the industry’s first automated machine learning capability that gives you complete control and visibility into your ML models. SageMaker Autopilot automatically inspects raw data, applies feature processors, picks the best set of algorithms, trains and tunes multiple models, tracks their performance, and then ranks the models, all with just a few clicks.
Amazon SageMaker Notebooks are one-click, sharable Jupyter notebooks that can be spun up quickly. You can also choose from dozens of pre-built notebooks within SageMaker or hundreds of algorithms and pre-trained models available in AWS Marketplace.
Amazon SageMaker supports the leading deep learning frameworks
Getting started blogs Bring Your Own Model for Labeling Workflows | Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning | Bring Your Own MXNet or TensorFlow Model | Automatically Create High-Quality Machine Learning Models using Amazon SageMaker Autopilot | Select ML Instances on the Fly
Try GitHub exercises Example Notebooks | SageMaker Algorithms | SageMaker Processing | Labeling with Ground Truth
Train machine learning models
Amazon SageMaker makes it easy to train machine learning (ML) models by providing everything you need to tune and debug models and execute training experiments.
Amazon SageMaker Experiments helps you manage iterations by automatically capturing the input parameters, configurations, and results, and storing them as ‘experiments’. You can work within the visual interface of SageMaker Studio, where you can browse active experiments, search for previous experiments by their characteristics, review previous experiments with their results, and compare experiment results visually.
Amazon SageMaker Debugger makes the training process more transparent by automatically capturing real-time metrics during training such as training and validation, confusion matrices, and learning gradients to help improve model accuracy. The metrics from SageMaker Debugger can be visualized in SageMaker Studio for easy understanding. SageMaker Debugger can also generate warnings and remediation advice when common training problems are detected.
You can reduce the costs of training your machine learning models by up to 90% using Managed Spot Training. Managed spot training uses Amazon EC2 Spot instances so your training jobs run at much lower costs compared to Amazon EC2 On-Demand instances.
Getting started blogs Amazon SageMaker Debugger – Debug Your Machine Learning Models | Amazon SageMaker Experiments – Organize, Track And Compare ML Experiments | ML Explainability with Amazon SageMaker Debugger | Run Distributed TensorFlow Training
Try GitHub exercises Hyperparameter Model Tuning | SageMaker Debugger
Deploy machine learning models
Amazon SageMaker makes it easy to generate predictions by providing everything you need to deploy machine learning models in production and monitor model quality.
Amazon SageMaker Model Monitor allows you to detect and remediate concept drift. Today, one of the big factors that can affect the accuracy of deployed models is if the data being used to generate predictions differs from data used to train the model. For example, changing economic conditions could drive new interest rates affecting home purchasing predictions. This is called concept drift, whereby the patterns the model uses to make predictions no longer apply. SageMaker Model Monitor automatically detects concept drift in deployed models and provides detailed alerts that help identify the source of the problem. All models trained in SageMaker automatically emit key metrics that can be collected and viewed in SageMaker Studio. From inside SageMaker Studio you can configure data to be collected, how to view it, and when to receive alerts.
Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. Amazon Augmented AI is a service that makes it easy to build the workflows required for human review of ML predictions. You can create your own workflows for models built on Amazon SageMaker using Amazon Augmented AI.
After a model is deployed, you can optimize infrastructure usage with Amazon Elastic Inference. Elastic Inference allows you to attach just the right amount of GPU-powered inference acceleration to any Amazon SageMaker instance type.
Getting started blogs Reduce ML Inference Costs for PyTorch Models | Automate Model Retraining & Deployment | Amazon SageMaker Model Monitor – Fully Managed Automatic Monitoring For Your Machine Learning Models | Simplify ML Inference on Kubernetes
Try GitHub exercises Automate Custom Model Deployment | SageMaker Model Monitor
