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.

Product Features

Building - Improve productivity using Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. 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, making you much more productive. All ML development activities including notebooks, experiment management, automatic model creation, debugging, and model drift detection can be performed within the unified SageMaker Studio visual interface.
Experiment management and tracking. Machine learning is an iterative process based on continuous experimentation, for instance, trying new learning algorithms or tweaking algorithm’s hyperparameters, all the while observing the impact of such incremental changes on model performance and accuracy. Over time this explosion of data makes it harder to track the best performing models, observations and lessons learned during the course of experimentation, and also the exact ingredients and recipe that went into creating those models in the first place. Amazon SageMaker Experiments helps you track, evaluate, and organize training experiements in an easy and scalable manner. SageMaker Experiments comes within the Amazon SageMaker Studio as well as a Python SDK with deep Jupyter integrations.
Model monitoring. Machine learning models are typically trained and evaluated using historical data but their quality degrades after they are deployed in production. This is because the distribution of the data sent to models for predictions can vary from the distribution of data used during training. The validity of prediction results can change over time and errors can be introduced upstream which can impact model quality. To prevent this, you need to monitor the quality of the models in production, identify issues quickly, and take corrective actions such as auditing or retraining models. To achieve this, you need to build tooling to store prediction related data securely, followed by implementing various statistical techniques to analyze this data and evaluate the quality of the model. Finally, you need to detect deviations in model quality to take the right corrective actions. As an alternative to building additional tooling, retraining models at a regular frequency is done which can be expensive.

Product Details

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.

Amazon SageMaker Experiments helps you organize and track iterations to machine learning models. Training an ML model typically entails many iterations to isolate and measure the impact of changing data sets, algorithm versions, and model parameters. You produce hundreds of artifacts such as models, training data, platform configurations, parameter settings, and training metrics during these iterations. Often cumbersome mechanisms like spreadsheets are used to track these experiments.

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