Amazon SageMaker
Amazon SageMaker, introduced in 2017, is a fully managed service for building, training, and deploying machine learning models. It includes tools like SageMaker Studio for end-to-end model development and SageMaker Autopilot for automating ML workflows.
https://aws.amazon.com/sagemaker
Amazon SageMaker was introduced in 2017. It is a fully managed service for machine learning (ML) that provides tools to build, train, and deploy ML models at scale. With integrated notebooks, algorithms, and model hosting, Amazon SageMaker simplifies the machine learning workflow.
The service supports various frameworks like TensorFlow, PyTorch, and Apache MXNet, enabling flexibility for developers and data scientists. Features like AutoML and SageMaker Neo further enhance productivity by automating model tuning and optimizing performance for edge devices, respectively.
https://aws.amazon.com/sagemaker/
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. It provides an integrated Jupyter notebook interface for easy access to data sources for exploration and analysis, so you don't need to manage servers. SageMaker also offers flexible distributed training options that adjust to your specific workflows, automatic model tuning for optimizing model performance, and a variety of built-in algorithms and support for popular frameworks like TensorFlow, PyTorch, and MXNet. Once the models are ready, SageMaker enables easy deployment in production so that you can start generating predictions for real-time or batch data.
- Snippet from Wikipedia: Amazon SageMaker
Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. It can be used to deploy ML models on embedded systems and edge-devices. The platform was launched in November 2017.
