Explore projects
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potato-oss / Djangae / Djangae
BSD 3-Clause "New" or "Revised" LicenseThe best way to run Django on Google Cloud
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Kanedias / MARC-FS
GNU General Public License v3.0 onlyFUSE filesystem attempt for Mail.Ru Cloud
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DBT image definition with services for RPC and documentation daemons - healdth checks and work directory change watcher to reload documentation
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fuelsave-os / ecs-mqtt-stream
Apache License 2.0a minimal example of a scalable, high available mqtt stream, ready to be deployed on AWS ECS
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Christian Drexler / hetznercloud
GNU General Public License v3.0 onlysimple cmd line interface to Hetzner cloud
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Manage operations on DigitalOcean spaces' objects.
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containalytics / containalytics
GNU Affero General Public License v3.0"Cloud container data analytics, statistical modeling, and machine learning on distributed databases". "A free opensource alternative to SPSS, SAS, MATLAB, PowerBI, Tableau and Alteryx". Runs on Linux, Windows, MacOS, and in the cloud via containers.
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Gira / kubedev - Kubernetes DevOps
MIT LicenseDevOps command line tool that standardizes workflows for Microservices in Kubernetes for teams: Develop, Build, Secure, Deploy.
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Codemotion - Oracle Hack and Secure the Workplace Hackathon
HealthGuard is an API platform which uses Artificial Intelligence and Machine Learning techniques to perform personal protective equipment and symptoms analysis for a Low-touch Economy
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AWS is the largest cloud platform. There are many different ways to approach a problem using AWS technology. These are some well-travelled roads and some unique corners of AWS.
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Azure is already solving challenging problems related to operationalising machine learning, from registering and versioning datasets to facilitating monitoring and deploying live inferencing models on scalable clusters.
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The number one advantage of using the Google Cloud may be that its technology is ideal for a multicloud strategy: Exploring GCP for use in MLOps.
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