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An AWS program offering students and educators no-cost access to cloud computing skills training developed by AWS experts, plus career pathways for students. It provides a library of free, self-paced online training and hands-on labs covering storage, compute, networking, databases and cloud operations. Learners as young as 13 can register with an email address, and learners 18 and older can access a job board.
Epsilla is a high-performance vector database hosted on GitHub under the epsilla-cloud organization. It leverages parallel graph computing for its operations, and offers an experimental option to use it as a Python library without requiring a Docker image, alongside Epsilla Cloud as a deployment option.
Kubeflow is described as the cloud native AI platform, composed of modular open source projects forming a Kubernetes-native stack for data and AI workloads. Its subprojects include Kubeflow Pipelines for building and deploying portable machine learning workflows, Kubeflow Trainer for distributed AI training and LLM fine-tuning, Kubeflow Katib for automated machine learning, Kubeflow Notebooks for interactive development environments, and Kubeflow Hub for indexing models and metadata. It is a Cloud Native Computing Foundation project.
Cognitive Class AI by IBM is a free online learning platform. It offers over 100 courses, 300 hands-on projects and 20 learning paths covering AI, cloud computing, full-stack development, cybersecurity, data science and other technology fields. Learners can earn certificates and badges, and the platform serves over 3 million learners.
Dask is an open-source Python library for parallel computing maintained by contributors across dozens of companies. The site describes uses including Dask DataFrames for pandas, parallel for loops, large arrays with NumPy and Xarray, and machine learning. It can run on a laptop or be deployed on resource managers such as Kubernetes or cloud.
dstack is an open framework for confidential AI that runs containers inside confidential VMs with Intel TDX, using existing Docker Compose files. It supports attested workload identities and native NVIDIA Confidential Computing on H100 and Blackwell GPUs, with options for self-hosted TDX machines or Phala Cloud.
KServe is a standardized distributed generative and predictive AI inference platform for scalable, multi-framework deployment on Kubernetes. It is a Cloud Native Computing Foundation incubating project. The platform supports backends such as vLLM and llm-d for serving LLMs, and multi-framework predictive serving. Standalone installation on standard Kubernetes is described as more lightweight but without canary deployment or request-based autoscaling with scale-to-zero.
MindSpore is an open source deep learning training and inference framework that could be used for mobile, edge, and cloud computing scenarios. Its source code is hosted in the mindspore-ai/mindspore repository on GitHub.
