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Join the chat at .NET Core mlops.neticon

MLOps.NET is a data science tool to track and manage the lifecycle of a ML.NET machine learning model.

  • Experiment tracking (SQLite, SQLServer, CosmosDb)
    • Experiments
    • Runs
    • Training time
    • Evaluation metrics
    • Hyper parameters
  • Data tracking
    • Data schema
    • Data quantity
    • Data hash
    • Data distribution
  • Model repostiory (Azure Blob Storage, AWS S3, local)
    • Run artifacts
    • Versioned registered models
  • Model deployment (Azure Blob Storage, AWS S3, local)
    • URI based deployment
    • Containerized deployments to a Kubernetes cluster
    • Manual deployment (in roadmap)

A client application to vizualize and manage the ML lifecycle is currently in the roadmap to be worked on.


Getting started

MLOps.NET revolves around an MLOpsContext. The MLOpsContext contains catalogs for e.g.Lifecycle, Data, Training, Evaluation and Deployment to access operations helpful to manage your model's lifecycle.

To create an MLOpsContext, use the MLOpsBuilder with your desired configuration. You can mix and match the location of your model repository and metadata store as you please.

Azure with CosmosDb

  IMLOpsContext mlOpsContext = new MLOpsBuilder()
    .UseCosmosDb("accountEndPoint", "accountKey")

SQL Server with Local model repository

  IMLOpsContext mlOpsContext = new MLOpsBuilder()

AWS with SQLite

  IMLOpsContext mlOpsContext = new MLOpsBuilder()
    .UseAWSS3ModelRepository("awsAccessKey", "awsSecretAccessKey", "regionName")

With a Container Registry and a Kubernetes Cluster

  IMLOpsContext mlOpsContext = new MLOpsBuilder()
    .UseContainerRegistry("RegistryName", "UserName", "Password")

Experiment tracking

To manage the lifecycle of a model, we'll need to track things such as the model's evaluation metrics, hyper-parameters used during training and so forth. We organize this under the concept of experiments and runs. An experiment is the logical grouping of a model we are trying to develop, e.g. a fraud classifier or recommendation engine. For a given experiment, we can create a number of runs. Each run represents one attempt to train a given model, which is associated with the run conditions and evaluation metrics achieved.

To create an Experiment and a Run, access the Lifecycle catalog on the MLOpsContext

  var experimentId = await mlOpsContext.LifeCycle.CreateExperimentAsync();

  var run = await mlOpsContext.LifeCycle.CreateRunAsync(experimentId, "{optional Git SHA}");

For simplicity, you can also create an experiment (if it does not yet exist) and a run in one line

  var run = await mlOpsContext.LifeCycle.CreateRunAsync(experimentName: "FraudClassifier", "{optional Git SHA}");

With an Experiment and a Run created, we can track the model training process.


You can access the operations necessary to track hyperparameters on the Training catalog. You can either track individual hyperparameters, such as number of epocs as follows:

  await mlOpsContext.Training.LogHyperParameterAsync(runId, "NumberOfEpochs", epocs);

Alternatively, you can pass in the entire appended trainer and MLOps.NET will automatically log all of the trainer's hyperparameters for you

  await mlOpsContext.Training.LogHyperParameterAsync<SdcaLogisticRegressionBinaryTrainer>(runId, trainer);
Evaluation metrics

You can access the operations necessary to track evaluation metrics on the Evaluation catalog. Similarly to tracking hyperparameters, you can either log individual evaluation metrics as follows:

  await mlOpsContext.Evaluation.LogMetricAsync(runId, "F1Score", 0.99d);

Alternatively, you can pass the entire ML.NET evaluation metric result and MLOps.NET will log all related evaluation metrics for you automatically.

  await mlOpsContext.Evaluation.LogMetricsAsync<CalibratedBinaryClassificationMetrics>(runId, metric);

Data tracking

There are a number of useful methods on the Data catalog to track the data used for training. This will give you a nice audit trail to understand what data was used to train a specific model, as well as how the data looked and if it has changed in between models.

To log the data schema and the data hash (to be used to compare data for two different models), you can use the LogDataAsync method

  await mlOpsContext.Data.LogDataAsync(runId, dataView);

To log the distribution of a given column, e.g. how many rows in a given dataset are positive and how many are negative, use the LogDistributionAsync method

  await mlOpsContext.Data.LogDataDistribution<bool>(run.RunId, dataView, nameof(Review.Sentiment));

Model repository

The end product of any model development effort is the actual model itself. MLOps.NET offers the ability to store your model either in a storage account in Azure, an S3 bucket in AWS or locally on a fileshare of your choosing.

To upload a model from a run

  var runArtifact = await mlOpsContext.Model.UploadAsync(runId, "pathToModel");

To register a model for deployment

  var registeredModel = await mlOpsContext.Model.RegisterModel(experimentId, runArtifact.RunArtifactId, registeredBy: "John Doe", description: "Altered weights");

Model deployment

Once a model has been registered, it's possible to deploy it to a given deployment target. A deployment target can be thought of as a specific environment from which you can serve your model, e.g. Test, Stage and Production. MLOps.NET currently supports deploying the model to an URI so that an ASP.NET Core application can consume it, or to a Kubernetes cluster so that the model can be consumed through a RESTful endpoint.

Methods to deploy a model can be found on the Deployment catalog. To deploy a model, start by creating a deployment target:

var deploymentTarget = await mlOpsContext.Deployment.CreateDeploymentTargetAsync(deploymentTargetName: "Test", isProduction: false);
Deploy a model to a URI

Given a deployment target and a registered model, you can then deploy the model to a URI

  var deployment = await mlOpsContext.Deployment.DeployModelToUriAsync(deploymentTarget, registeredModel, deployedBy: "John Doe");

The model is deployed to deployment.DeploymentUri, which can be used by a consuming application. It's also possible to get the URI/path to deployed model by doing the following:

  var deployment = await mlOpsContext.Deployment.GetDeployments()
    .FirstOrDefault(x => x.DeploymentTarget.Name == "Test");

  var deploymentUri = await mlOpsContext.Deployment.GetDeploymentUri(deployment);

Deploying a model for an experiment to a given deployment target, e.g. Test, will automatically overwrite the existing model, thus the consuming application will not need to update it's URI/path to the model it's consuming. ML.NET will automatically poll for changes to the file making it seamless and allowing the consuming application and the ML.NET model to have different release cycles.

Deploy a model to Kubernetes

To deploy a model to Kubernetes you'll need to configure a Container Registry and a Kubernetes cluster via the MLOpsBuilder. MLOps.NET is agnostic of cloud provider so you can have your container registry either live locally or in the cloud (private/public). You are free to host your Kubernetes cluster either in Azure, AWS or elsewhere, the tool simply finds it using the provided kubeconfig. Note that the UseKubernetes method either takes the absolute path to the kubeconfig or the content of the kubeconfig itself, which can be useful if we are configuring it via a CI pipeline.

  IMLOpsContext mlOpsContext = new MLOpsBuilder()
    .UseContainerRegistry("RegistryName", "UserName", "Password")

We can then deploy the model to the Kubernetes cluster

  var deployment = await sut.Deployment.DeployModelToKubernetesAsync<ModelInput, ModelOutput>(deploymentTarget, registeredModel, "deployedBy");

If you don't know the ModelInput and ModelOutput at deployment time, you can register the model schema during the run

  await sut.LifeCycle.RegisterModelSchema<ModelInput, ModelOutput>(run.RunId);

This simplifies the call at deployment time

  var deployment = await sut.Deployment.DeployModelToKubernetesAsync(deploymentTarget, registeredModel, "deployedBy");


We welcome contributors! Before getting started, take a moment to read our contributing guidelines as well as the docs for new developers on how to set up your local environment.

Code of Conduct

Please take a moment to read our code of conduct