Best AI Development Apps for Android of 2024

Find and compare the best AI Development apps for Android in 2024

Use the comparison tool below to compare the top AI Development apps for Android on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    RunLve Reviews
    Runlve is at the forefront of the AI revolution. We provide data science, MLOps and data & models management to empower our community and customers with AI capabilities that will propel their projects forward.
  • 2
    PyTorch Reviews
    TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
  • 3
    Ikigai Reviews
    Simulations based on historical data can be used to improve models and update them incrementally. Data governance, access control, and versioning allow for easy collaboration. Ikigai has a wide range of integrations that make it easy to integrate with tools already in your workflow. Ikigai has 200+ connectors that allow you to connect to almost any data source. Want to push your ML to a dashboard or website? Integrate directly using Ikigai’s web integrations. Triggers can be used to run data synchronizations, and retrieve updates every time you run an automation flow. You can integrate Ikigai seamlessly by using your own APIs or creating APIs for your data stack.
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