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AWS Compute Optimizer recommends optimal AWS Compute resources for your workloads to reduce costs and improve performance by using machine learning to analyze historical utilization metrics. Over-provisioning compute can lead to unnecessary infrastructure cost and under-provisioning compute can lead to poor application performance. Compute Optimizer helps you choose the optimal Amazon EC2 instance types, including those that are part of an Amazon EC2 Auto Scaling group, based on your utilization data.
By applying the knowledge drawn from Amazon’s own experience running diverse workloads in the cloud, Compute Optimizer identifies workload patterns and recommends optimal compute resources. Compute Optimizer analyzes the configuration and resource utilization of your workload to identify dozens of defining characteristics, for example, if a workload is CPU-intensive, or if it exhibits a daily pattern or if a workload accesses local storage frequently. The service processes these characteristics and identifies the hardware resource headroom required by the workload. Compute Optimizer infers how the workload would have performed on various hardware platforms (e.g. Amazon EC2 instances types) and offers recommendations.
Code Ocean is an innovative platform that facilitates the reproducibility and collaboration of scientific research. It offers a workspace where researchers can develop, share, and execute their computational experiments seamlessly.
Code Ocean streamlines scientific research by offering a cloud-based platform for developing and executing code-based experiments. It serves as a vital tool for researchers seeking to enhance collaboration and reproducibility in their work. By integrating various tools and supporting multiple programming languages, Code Ocean allows researchers to efficiently manage and share their computations, fostering a more collaborative scientific ecosystem.
What are Code Ocean's most important features?Industries like biotechnology, academia, and pharmaceuticals find Code Ocean vital for managing complex computations. In biotechnology, it accelerates research timelines. Academic institutions use it to improve reproducibility in studies while pharmaceuticals rely on it for data accuracy and collaborative R&D efforts.
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