AIM-AHEAD Service WorkBench on AWS

Service Workbench on AWS (SWB) promotes infrastructure equity by providing the same analytical tools and level of access to researchers and students. The only infrastructure required to fully leverage this resource is a simple laptop and an internet connection.
SWB provides researchers and students with a user-friendly environment to configure and deploy their own secure cloud-computing environment in a few clicks. SWB is the first Apache 2.0 open-source native cloud computing platform that provides a modular and scalable solution to the supply of computing environments for researchers and students. The platform supplies students and research teams with a simple web application, empowering them to easily deploy and access any cloud workspace from a custom catalog of pre-configured and extendible) environments ( R, Jupyter Notebooks, Python, etc…) leveraging all AWS advanced data analysis tools and native security controls.
To get started with an analysis, end-users are only required to connect to the web application and select their desired configuration. The research workspace will be deployed in two clicks, selecting first the type of workspace and second the most applicable configuration for their analysis in terms of instance type, memory, CPUs, and GPUs. Researchers will have access to the computing power they need, regardless of the technical underlying complexity of it. Moreover, there is a growing open community supporting various SWB workspaces, which enables the deployment of any type of computing workspaces.


Examples of the resources available - out-of-the-box - in SWB: AWS SageMaker instances that work with widely used Jupyter Notebook formats. Moreover, SageMaker instances come with staple ML/DL libraries (e.g., TensorFlow, PyTorch, MxNet), allowing savvy users to get started right away. On the opposite, non AI/ML experts can discover all pre-configured computing environments featuring many tutorials and analysis examples using public data in the form of Jupyter Notebooks for anyone to start learning using those resources. This AI/ML service accelerates 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. SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML, and enables the Fellows to develop and serve their own models.


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