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  • MongoDB Atlas runs apps anywhere Icon
    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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  • Train ML Models With SQL You Already Know Icon
    Train ML Models With SQL You Already Know

    BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

    Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
    Try Free
  • 1
    MicroK8s

    MicroK8s

    Single-package Kubernetes for developers, IoT and edge

    Low-ops, minimal production Kubernetes, for devs, cloud, clusters, workstations, Edge and IoT. MicroK8s automatically chooses the best nodes for the Kubernetes datastore. When you lose a cluster database node, another node is promoted. No admin needed for your bulletproof edge. MicroK8s is small, with sensible defaults that ‘just work’. A quick install, easy upgrades and great security make it perfect for micro clouds and edge computing. As the publishers of MicroK8s, we deliver the world’s...
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  • 2
    Spilo

    Spilo

    Highly available elephant herd: HA PostgreSQL cluster using Docker

    ...It includes components for failover, streaming replication, backups, and connection pooling. Spilo is used in production by Zalando and is designed to provide a resilient, self-healing Postgres cluster with minimal manual intervention.
    Downloads: 0 This Week
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  • 3
    DeepCluster

    DeepCluster

    Deep Clustering for Unsupervised Learning of Visual Features

    DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups.
    Downloads: 0 This Week
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