AmazonMachine Learning end-to-end plateform

Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.

Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows.


Prepare Data in Minutes: Using Amazon SageMaker Data Wrangler, you can quickly and easily prepare data and create model features. You can connect to data sources and use built-in data transformations to engineer model features.
Transparency: Amazon SageMaker Clarify provides data to improve model quality through bias detection during data preparation and after training. SageMaker Clarify also provides model explainability reports so stakeholders can see how and why models make predictions.
Security and Privacy: Amazon SageMaker allows you to operate on a fully secure ML environment from day one. You can use a comprehensive set of security features to help support a broad range of industry regulations.
Data Labeling: Amazon SageMaker Ground Truth makes it easy to build highly accurate training datasets for machine learning. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows including 3D point clouds, video, images, and text.
Feature Store: Amazon SageMaker Feature Store is a purpose-built feature store for ML serving features in both real-time and in batch. You can securely store, discover, and share features so you get the same features consistently both during training and during inference, saving months of development effort.
Data Processing at Scale: Amazon SageMaker Processing extends the ease, scalability, and reliability of SageMaker to running data processing workloads. SageMaker Processing allows you to connect to existing storage, spin up the resources required to run your job, save the output to persistent storage, and provides logs and metrics.
One-click Jupyter Notebooks: Amazon SageMaker Studio Notebooks are one-click Jupyter notebooks and the underlying compute resources are fully elastic, so you can easily dial up or down the available resources. Notebooks are shared with a single click so colleagues get the same notebook, saved in the same place.
Built-in Algorithms: Amazon SageMaker also offers over 15 built-in algorithms available in pre-built container images that can be used to quickly train and run inference.
Pre-Built Solutions and Open-Source Models: Amazon SageMaker JumpStart helps you quickly get started with ML using pre-built solutions that can be deployed with just a few clicks. SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models.

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