Register & Train
Register & Train
  • Upload your production ready
    models using simple API, or train
    models with MLOne
  • Train multiple models
    at the same time
  • Distribute training of large
    generative models using multiple
    GPU instances
  • Easily provide your team
    with a wide range of multiple GPUs hardware configurations
  • Organize versioning of data,
    code and parameters
    for every ML build
  • Ensure your models are well
    organized and easy to manage
    with the model registry


Deploy
Deploy
  • With a few clicks, create a fault-tolerant, highly available and reliable cloud production environment
  • Serve models on the independent instances or on the elastic cluster
  • Build auto-scalable ML-services right from Jupyter
  • Use your favorite machine learning stack: Pytorch, Tensorflow, Keras, MLFlow, SKlearn, Spark-mlib, triton, onnx and many others
  • Schedule services to cut costs
  • Use secured inference endpoints out of the box
  • With a few clicks, create a fault-tolerant, highly
    available and reliable cloud production environment
  • Serve models on the independent instances
    or on the elastic cluster
  • Build auto-scalable ML-services right from
    Jupyter
  • Use your favorite machine learning stack: Pytorch, Tensorflow, Keras, MLFlow, SKlearn, Spark-mlib, triton, onnx and many others
  • Schedule services to cut costs
  • Use secured inference endpoints out of the box


Monitor
Monitor
  • 1
    Track models metrics, service performance, and resources usage with powerful visualization tools
  • 2
    Compare the baseline and the inference data to make sure model actuality
  • 3
    Automatically detect data and concept drifts to manage model lifecycle
  • 4
    Quickly react to customizable alerts when thresholds are exceeded


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