1. Alternatives
  2. Β β†’Β Google CLOUD AUTOML

Google CLOUD AUTOML alternatives and competitors

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Train high quality custom machine learning models with minimum effort and machine learning expertise.

Top alternatives for Google CLOUD AUTOML

Automate SOC 2, ISO 27001, and HIPAA compliance in weeks
  • Apple Core ML

    Integrate a broad variety of ML model types into your app
  • Botsify 2.0

    11 reviews

    Botsify helps people create artificial intelligent conversational chatbots without having to code or program. The platform offers several integrations out of the box and works on multiple platforms including facebook messenger and website.

    "This platform provides so many integrations (within the chatbot you make) like Google search, Shopify, Zapier and more. I use Shopify to co…

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  • TensorFlow Lite

    TensorFlow’s lightweight solution for mobile and embedded devices. TensorFlow has always run on many platforms but as the adoption of ML models has grown exponentially over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models.

  • Roboflow Universe

    2 reviews
    You no longer need to collect and label images or train a ML model to add computer vision to your project. Start from the finish line by using one of the dozens of pre-trained models our community has already trained and shared via a simple API.
  • IBM Watson for CoreML

    IBM Watson now integrates directly into Apple's ML Core, so developers can build AI-powered apps to help improve every part of your life. πŸ€–


    2 reviews
    GPU.LAND offers GPU instances for Deep Learning β€” at dirt-cheap prices. How cheap? Up to 80% cheaper vs major cloud providers. We believe everyone, and not just "Big Tech", should be able to train AI models. So we built the cheapest place on the web to do it.
    Get it
  • Foundations Atlas

    3 reviews
    Atlas allows you to run, track & evaluate machine learning experiments on your infrastructure.
    Currently used by 700+ data scientists, Atlas helps run experiments concurrently & supports the usage of preemptible/spot instances, saving you up to 8x in GPU cost
  • 3D semantic segmentation by Playment

    Playment's new release of 3D point cloud Segmentation toolkit enables you to generate high-quality training data to build 3D perception models.
  • QuickAI

    QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models. QuickAI can reduce what would take tens of lines of code into 1-2 lines. This makes fast experimentation very easy and clean.
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