Showing 3 open source projects for "machine learning projects"

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    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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    $300 Free Credits to Build on Google Cloud

    New customers can spin up VMs, build with AI, and query data at no cost.

    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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    MindsDB

    MindsDB

    Making Enterprise Data Intelligent and Responsive for AI

    MindsDB is an AI data solution that enables humans, AI, agents, and applications to query data in natural language and SQL, and get highly accurate answers across disparate data sources and types. MindsDB connects to diverse data sources and applications, and unifies petabyte-scale structured and unstructured data. Powered by an industry-first cognitive engine that can operate anywhere (on-prem, VPC, serverless), it empowers both humans and AI with highly informed decision-making...
    Downloads: 10 This Week
    Last Update:
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    MCP HFSpace

    MCP HFSpace

    MCP Server to Use HuggingFace spaces, easy configuration

    The MCP-HFSpace server is an MCP server designed to connect AI assistants with Hugging Face Spaces. It simplifies the configuration process and is compatible with Claude Desktop mode, allowing for seamless integration and utilization of Hugging Face's machine learning models and applications. ​
    Downloads: 4 This Week
    Last Update:
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  • 3
    Colab-MCP

    Colab-MCP

    An MCP server for interacting with Google Colab

    ...Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution.
    Downloads: 0 This Week
    Last Update:
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