Showing 3 open source projects for "ai code offline"

View related business solutions
  • Run Any Workload on Compute Engine VMs Icon
    Run Any Workload on Compute Engine VMs

    From dev environments to AI training, choose preset or custom VMs with 1–96 vCPUs and industry-leading 99.95% uptime SLA.

    Compute Engine delivers high-performance virtual machines for web apps, databases, containers, and AI workloads. Choose from general-purpose, compute-optimized, or GPU/TPU-accelerated machine types—or build custom VMs to match your exact specs. With live migration and automatic failover, your workloads stay online. New customers get $300 in free credits.
    Try Compute Engine
  • Ship AI Apps Faster with Vertex AI Icon
    Ship AI Apps Faster with Vertex AI

    Go from idea to deployed AI app without managing infrastructure. Vertex AI offers one platform for the entire AI development lifecycle.

    Ship AI apps and features faster with Vertex AI—your end-to-end AI platform. Access Gemini 3 and 200+ foundation models, fine-tune for your needs, and deploy with enterprise-grade MLOps. Build chatbots, agents, or custom models. New customers get $300 in free credit.
    Try Vertex AI Free
  • 1
    AI Researcher

    AI Researcher

    An autonomous AI researcher

    AI Researcher is an experimental open-source project that demonstrates how multiple AI agents can collaborate to conduct complex research tasks from start to finish with minimal human intervention. It orchestrates agents that can generate research questions, perform literature reviews, execute experiments, analyze results, and synthesize findings into structured outputs like reports or code.
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    Recommenders

    Recommenders

    Best practices on recommendation systems

    The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    Brain Tokyo Workshop

    Brain Tokyo Workshop

    Experiments and code from Google Brain’s Tokyo research workshop

    ...The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. Overall, this repository serves as an open research hub for sharing ideas and advancing the understanding of intelligent systems.
    Downloads: 2 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next
MongoDB Logo MongoDB