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it's easy
Handle your models effortlessly with one click
  • Train models
    Upload your production ready models using simple API or train models with MLOne
    1
  • Use multiple GPU
    Distribute training of large generative models using multiple GPU instances
    2
  • Simultaneous training
    Train multiple models simultaneously
    3
  • Well-organized
    Ensure your models are well-organized and easy to manage with a model registry
    4
  • Easy
    Easily provide a wide range of hardware configurations with GPUs/CPUs to your team
    5
  • Versioning
    Organize versioning of data, code and parameters for every ML build
    6
it's flexible
Quickly and easily deploy online and batch ML models into production
  • Just a few clicks
    With a few clicks build a fault tolerant, highly available and reliable cloud based production environment
  • Elastic cluster
    Serve models on the independent instances or on the elastic cluster
  • Auto-scale
    Build auto-scalable ML-services directly from Jupyter
  • ML stack
    Use your favorite ML stack: Pytorch, Tensorflow, Keras, MLFlow, SKlearn, Spark-mlib, triton, onnx and
    many others
  • Reducing the costs
    Schedule running of your services to reduce the costs
  • Secure
    Use secured inference endpoints out of the box
it's productive
Monitor the performance of deployed models.
Detect concept and data drifts in a timely manner
  • Tools
    Track models metrics, services performance
    and resources utilization using the powerful visualization tools
    1
  • Baseline
    Compare baseline and inference data to ensure model actuality
    2
  • Lifecycle
    Automatically detect data and concept drifts to the manage lifecycle of
    the model
    3
  • Alerts
    React quickly to configurable alerts when thresholds exceeded
    4

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by clicking above you are agreeing to our
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