---
title: Introduction
---

`clearml-serving` is a command line utility for model deployment and orchestration.
It enables model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based 
solution.


## Features

* Easy to deploy & configure
    * Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
    * Support Deep Learning Models (TensorFlow, PyTorch, ONNX)
    * Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
* Flexible
    * On-line model deployment
    * On-line endpoint model/version deployment (i.e. no need to take the service down)
    * Per model standalone preprocessing and postprocessing python code
* Scalable
    * Multi model per container
    * Multi models per serving service
    * Multi-service support (fully separated multiple serving service running independently)
    * Multi cluster support
    * Out-of-the-box node autoscaling based on load/usage
* Efficient
    * Multi-container resource utilization
    * Support for CPU & GPU nodes
    * Auto-batching for DL models
* [Automatic deployment](clearml_serving_tutorial.md#automatic-model-deployment)
    * Automatic model upgrades w/ canary support
    * Programmable API for model deployment
* [Canary A/B deployment](clearml_serving_tutorial.md#canary-endpoint-setup) - online Canary updates
* [Model Monitoring](clearml_serving_tutorial.md#model-monitoring-and-performance-metrics)
    * Usage Metric reporting
    * Metric Dashboard
    * Model performance metric
    * Model performance Dashboard

## Components

![ClearML Serving](https://github.com/allegroai/clearml-serving/raw/main/docs/design_diagram.png?raw=true)

* **CLI** - Secure configuration interface for on-line model upgrade/deployment on running Serving Services

* **Serving Service Task** - Control plane object storing configuration on all the endpoints. Supports multiple separate 
  instances, deployed on multiple clusters.

* **Inference Services** - Inference containers, performing model serving pre/post-processing. Also supports CPU model 
  inferencing.

* **Serving Engine Services** - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference 
  Services for heavier model inference.

* **Statistics Service** - Single instance per Serving Service collecting and broadcasting model serving & performance 
  statistics

* **Time-series DB** - Statistics collection service used by the Statistics Service, e.g. Prometheus

* **Dashboards** - Customizable dashboard solution on top of the collected statistics, e.g. Grafana

![Grafana dashboard](../img/gif/clearml_serving_grafana_gif.gif)

## Next Steps

See ClearML Serving setup instructions [here](clearml_serving_setup.md). For further details, see the ClearML Serving 
[Tutorial](clearml_serving_tutorial.md).