# Seldon Core Release 0.4.0 A summary of the main contributions to the [Seldon Core release 0.4.0](https://github.com/SeldonIO/seldon-core/releases/tag/v0.4.0). ## Prepackaged Model Servers Seldon provides several prepacked servers you can use to deploy trained models: * [SKLearn Server](../servers/sklearn.html) * [XGBoost Server](../servers/xgboost.html) * [Tensorflow Serving](../servers/tensorflow.html) * [MLFlow Server](../servers/mlflow.html) For these servers you only need the location of the saved model in a local filestore, Google bucket or S3 bucket. An example manifest with an sklearn server is shown below: ``` apiVersion: machinelearning.seldon.io/v1alpha2 kind: SeldonDeployment metadata: name: sklearn spec: name: iris predictors: - graph: children: [] implementation: SKLEARN_SERVER modelUri: gs://seldon-models/sklearn/iris name: classifier name: default replicas: 1 ``` The `modelUri` specifies the bucket containing the saved model, in this case `gs://seldon-models/sklearn/iris`. `modeluri` supports the following three object storage providers: * Google Cloud Storage (using `gs://`) * S3-comptaible (using `s3://`) * Azure Blob storage (using `https://(.+?).blob.core.windows.net/(.+)`) ## Gunicorn Alpha Feature We have provided an early alpha release for the python language wrapper to run under [gunicorn](https://gunicorn.org/) rather than Flask. For further details see our [gunicorn documentation](../python/python_component.html#gunicorn-alpha-feature). ## Kustomize Integration We have a [kustomize resource](https://github.com/SeldonIO/seldon-core/tree/master/kustomize/seldon-core-operator) you can use and extend for your own particular setup for installing Seldon Core. ## More Example Integrations Our range of example has expanded to include: * [Tabular Model Explanations using Seldon Alibi](../examples/alibi_anchor_tabular.html) * [Alibaba MNIST](../examples/alibaba_ack_deep_mnist.html) * [MLFLow Model Server](../examples/mlflow_server_ab_test_ambassador.html)