seldon-core

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

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Quick Overview

Seldon Core is an open-source platform for deploying machine learning models on Kubernetes. It provides a flexible, scalable, and production-ready solution for serving ML models, offering features like A/B testing, canary deployments, and advanced monitoring capabilities.

Pros

Seamless integration with Kubernetes for scalable ML model deployment
Support for multiple ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
Advanced features like A/B testing, canary deployments, and explainers
Extensive monitoring and observability tools

Cons

Steep learning curve for those unfamiliar with Kubernetes
Complex setup process for advanced features
Limited support for edge deployment scenarios
Resource-intensive for small-scale projects

Code Examples

Creating a simple Seldon deployment:

from seldon_core.seldon_client import SeldonClient
import numpy as np

sc = SeldonClient(deployment_name="mymodel", namespace="seldon")

client_prediction = sc.predict(data=np.array([[1, 2, 3]]))
print(client_prediction)

Implementing a custom model:

class MyModel:
    def __init__(self):
        print("Initializing")

    def predict(self, X, features_names=None):
        print("Predict called")
        return X

    def metrics(self):
        return [
            {"type": "COUNTER", "key": "mycounter", "value": 1},
            {"type": "GAUGE", "key": "mygauge", "value": 100},
        ]

Creating a Seldon deployment using SeldonDeployment CRD:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: seldon-model
spec:
  name: test-deployment
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - name: classifier
          image: seldonio/sklearn-iris:0.1
    graph:
      name: classifier
      type: MODEL
    name: example
    replicas: 1

Getting Started

Install Seldon Core on your Kubernetes cluster:

kubectl create namespace seldon-system
helm install seldon-core seldon-core-operator \
    --repo https://storage.googleapis.com/seldon-charts \
    --set usageMetrics.enabled=true \
    --namespace seldon-system

Create a Seldon deployment (using the YAML example above):

kubectl apply -f seldon-deployment.yaml

Access your model:

kubectl port-forward svc/seldon-model-example 8000:8000
curl -X POST http://localhost:8000/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": { "ndarray": [[1,2,3,4]] } }'

Competitor Comparisons

kserve

3,449

Standardized Serverless ML Inference Platform on Kubernetes

Pros of KServe

Deeper integration with Kubernetes ecosystem and Knative
More extensive support for model serving frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
Built-in support for model explainability and drift detection

Cons of KServe

Steeper learning curve due to more complex architecture
Less flexibility in custom model deployment compared to Seldon Core
Requires Istio for full functionality, which can add complexity

Code Comparison

KServe example:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    sklearn:
      storageUri: "gs://kfserving-samples/models/sklearn/iris"

Seldon Core example:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: sklearn-iris
spec:
  predictors:
  - graph:
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/iris
    name: default

Both KServe and Seldon Core are powerful platforms for deploying machine learning models in Kubernetes environments. KServe offers tighter integration with the Kubernetes ecosystem and broader support for various frameworks, while Seldon Core provides more flexibility for custom deployments and a gentler learning curve.

mlflow

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Open source platform for the machine learning lifecycle

Pros of MLflow

More comprehensive ML lifecycle management, including experiment tracking and model registry
Language-agnostic with support for Python, R, Java, and more
Easier to set up and use for individual data scientists or small teams

Cons of MLflow

Less focused on production deployment and scaling of ML models
Limited built-in support for advanced serving features like A/B testing and canary deployments
Requires additional tools for robust production-grade model serving

Code Comparison

MLflow example:

import mlflow

mlflow.start_run()
mlflow.log_param("param1", 5)
mlflow.log_metric("accuracy", 0.85)
mlflow.end_run()

Seldon Core example:

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(deployment_name="mymodel", namespace="default")
response = sc.predict(data=X)
print(response)

MLflow focuses on tracking experiments and logging metrics, while Seldon Core is designed for deploying and serving models in production environments. MLflow provides a more comprehensive solution for the entire ML lifecycle, whereas Seldon Core excels in robust, scalable model deployment on Kubernetes.

cortex

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Production infrastructure for machine learning at scale

Pros of Cortex

Simpler deployment process with automatic infrastructure provisioning
Native support for AWS, reducing complexity for AWS users
Built-in autoscaling and GPU support out of the box

Cons of Cortex

Limited to AWS, while Seldon Core supports multiple cloud providers
Smaller community and ecosystem compared to Seldon Core
Less flexibility in terms of customization and integration options

Code Comparison

Cortex deployment example:

- name: iris-classifier
  predictor:
    type: python
    path: predictor.py
  compute:
    gpu: 1
    mem: 4G

Seldon Core deployment example:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris-model
spec:
  predictors:
  - graph:
      name: classifier
      implementation: SKLEARN_SERVER
    name: default

Both frameworks aim to simplify ML model deployment, but Cortex focuses on AWS-specific deployments with a more streamlined approach, while Seldon Core offers greater flexibility and multi-cloud support at the cost of increased complexity.

BentoML

7,069

The easiest way to serve AI apps and models - Build reliable Inference APIs, LLM apps, Multi-model chains, RAG service, and much more!

Pros of BentoML

Simpler setup and deployment process, especially for local development
Built-in support for a wider range of ML frameworks and libraries
More flexible API serving options, including REST, gRPC, and CLI

Cons of BentoML

Less mature ecosystem for large-scale production deployments
Fewer advanced features for monitoring and scaling in complex environments
Limited native support for A/B testing and canary deployments

Code Comparison

BentoML:

import bentoml

@bentoml.env(pip_packages=["scikit-learn"])
@bentoml.artifacts([bentoml.sklearn.SklearnModelArtifact('model')])
class SklearnIrisClassifier(bentoml.BentoService):
    @bentoml.api(input=bentoml.handlers.DataframeHandler())
    def predict(self, df):
        return self.artifacts.model.predict(df)

Seldon Core:

class IrisClassifier(object):
    def __init__(self):
        self.model = joblib.load('iris_model.joblib')

    def predict(self, X, features_names=None):
        return self.model.predict(X)

Both frameworks aim to simplify ML model deployment, but BentoML offers a more user-friendly approach for local development and supports a broader range of ML frameworks out-of-the-box. Seldon Core, on the other hand, provides more advanced features for production-grade deployments and integrates better with Kubernetes ecosystems.

modeldb

1,698

Open Source ML Model Versioning, Metadata, and Experiment Management

Pros of ModelDB

Focuses on model versioning and metadata tracking
Provides a user-friendly web interface for experiment management
Supports integration with popular ML frameworks like TensorFlow and PyTorch

Cons of ModelDB

Less emphasis on model deployment and serving compared to Seldon Core
May require additional tools for end-to-end MLOps workflows
Limited support for advanced deployment scenarios like A/B testing

Code Comparison

ModelDB (Python client):

from verta import Client

client = Client("http://localhost:3000")
proj = client.set_project("My Project")
expt = client.set_experiment("My Experiment")
run = client.set_experiment_run("My Run")

run.log_parameter("num_layers", 5)
run.log_metric("accuracy", 0.95)

Seldon Core (Deployment YAML):

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris-model
spec:
  predictors:
  - graph:
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/iris
    name: default

feast

5,552

The Open Source Feature Store for Machine Learning

Pros of Feast

Specialized in feature management and serving for machine learning
Supports multiple data sources and feature stores
Provides a unified API for offline and online feature access

Cons of Feast

Limited to feature management, not a complete MLOps solution
Requires additional tools for model deployment and serving
May have a steeper learning curve for teams new to feature stores

Code Comparison

Feast example:

from feast import FeatureStore

store = FeatureStore("feature_repo/")
features = store.get_online_features(
    features=["driver:rating", "driver:trips_today"],
    entity_rows=[{"driver_id": 1001}]
)

Seldon Core example:

from seldon_core.seldon_client import SeldonClient

sc = SeldonClient(deployment_name="mymodel", namespace="default")
response = sc.predict(data={"ndarray": [[1.0, 2.0, 5.0]]})

While Feast focuses on feature management and serving, Seldon Core provides a more comprehensive MLOps solution for model deployment and serving. Feast excels in feature engineering and storage, whereas Seldon Core offers broader capabilities for model deployment, A/B testing, and monitoring in production environments.

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README

Seldon Core: Blazing Fast, Industry-Ready ML

A platform to deploy your machine learning models on Kubernetes at massive scale.

Seldon Core V2 Now Available

Seldon Core V2 is now available. If you're new to Seldon Core we recommend you start here. Check out the docs here and make sure to leave feedback on our slack community and submit bugs or feature requests on the repo. The codebase can be found in this branch.

Continue reading for info on Seldon Core V1...

Overview

Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.

Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more.

Read the Seldon Core Documentation
Join our community Slack to ask any questions
Get started with Seldon Core Notebook Examples
Join our fortnightly online working group calls : Google Calendar
Learn how you can start contributing
Check out Blogs that dive into Seldon Core components
Watch some of the Videos and Talks using Seldon Core

High Level Features

With over 2M installs, Seldon Core is used across organisations to manage large scale deployment of machine learning models, and key benefits include:

Easy way to containerise ML models using our pre-packaged inference servers, custom servers, or language wrappers.
Out of the box endpoints which can be tested through Swagger UI, Seldon Python Client or Curl / GRPCurl.
Cloud agnostic and tested on AWS EKS, Azure AKS, Google GKE, Alicloud, Digital Ocean and Openshift.
Powerful and rich inference graphs made out of predictors, transformers, routers, combiners, and more.
Metadata provenance to ensure each model can be traced back to its respective training system, data and metrics.
Advanced and customisable metrics with integration to Prometheus and Grafana.
Full auditability through model input-output request logging integration with Elasticsearch.
Microservice distributed tracing through integration to Jaeger for insights on latency across microservice hops.
Secure, reliable and robust system maintained through a consistent security & updates policy.

Getting Started

Deploying your models using Seldon Core is simplified through our pre-packaged inference servers and language wrappers. Below you can see how you can deploy our "hello world Iris" example. You can see more details on these workflows in our Documentation Quickstart.

Install Seldon Core

Quick install using Helm 3 (you can also use Kustomize):

kubectl create namespace seldon-system

helm install seldon-core seldon-core-operator \
    --repo https://storage.googleapis.com/seldon-charts \
    --set usageMetrics.enabled=true \
    --namespace seldon-system \
    --set istio.enabled=true
    # You can set ambassador instead with --set ambassador.enabled=true

Deploy your model using pre-packaged model servers

We provide optimized model servers for some of the most popular Deep Learning and Machine Learning frameworks that allow you to deploy your trained model binaries/weights without having to containerize or modify them.

You only have to upload your model binaries into your preferred object store, in this case we have a trained scikit-learn iris model in a Google bucket:

gs://seldon-models/v1.19.0-dev/sklearn/iris/model.joblib

Create a namespace to run your model in:

kubectl create namespace seldon

We then can deploy this model with Seldon Core to our Kubernetes cluster using the pre-packaged model server for scikit-learn (SKLEARN_SERVER) by running the kubectl apply command below:

$ kubectl apply -f - << END
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris-model
  namespace: seldon
spec:
  name: iris
  predictors:
  - graph:
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/v1.19.0-dev/sklearn/iris
      name: classifier
    name: default
    replicas: 1
END

Send API requests to your deployed model

Every model deployed exposes a standardised User Interface to send requests using our OpenAPI schema.

This can be accessed through the endpoint http://<ingress_url>/seldon/<namespace>/<model-name>/api/v1.0/doc/ which will allow you to send requests directly through your browser.

Or alternatively you can send requests programmatically using our Seldon Python Client or another Linux CLI:

$ curl -X POST http://<ingress>/seldon/seldon/iris-model/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": { "ndarray": [[1,2,3,4]] } }'

{
   "meta" : {},
   "data" : {
      "names" : [
         "t:0",
         "t:1",
         "t:2"
      ],
      "ndarray" : [
         [
            0.000698519453116284,
            0.00366803903943576,
            0.995633441507448
         ]
      ]
   }
}

Deploy your custom model using language wrappers

For more custom deep learning and machine learning use-cases which have custom dependencies (such as 3rd party libraries, operating system binaries or even external systems), we can use any of the Seldon Core language wrappers.

You only have to write a class wrapper that exposes the logic of your model; for example in Python we can create a file Model.py:

import pickle
class Model:
    def __init__(self):
        self._model = pickle.loads( open("model.pickle", "rb") )

    def predict(self, X):
        output = self._model(X)
        return output

We can now containerize our class file using the Seldon Core s2i utils to produce the sklearn_iris image:

s2i build . seldonio/seldon-core-s2i-python3:0.18 sklearn_iris:0.1

And we now deploy it to our Seldon Core Kubernetes Cluster:

$ kubectl apply -f - << END
apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris-model
  namespace: model-namespace
spec:
  name: iris
  predictors:
  - componentSpecs:
    - spec:
        containers:
        - name: classifier
          image: sklearn_iris:0.1
    graph:
      name: classifier
    name: default
    replicas: 1
END

Send API requests to your deployed model

Every model deployed exposes a standardised User Interface to send requests using our OpenAPI schema.

This can be accessed through the endpoint http://<ingress_url>/seldon/<namespace>/<model-name>/api/v1.0/doc/ which will allow you to send requests directly through your browser.

Or alternatively you can send requests programmatically using our Seldon Python Client or another Linux CLI:

$ curl -X POST http://<ingress>/seldon/model-namespace/iris-model/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": { "ndarray": [1,2,3,4] } }' | json_pp

{
   "meta" : {},
   "data" : {
      "names" : [
         "t:0",
         "t:1",
         "t:2"
      ],
      "ndarray" : [
         [
            0.000698519453116284,
            0.00366803903943576,
            0.995633441507448
         ]
      ]
   }
}

Dive into the Advanced Production ML Integrations

Any model that is deployed and orchestrated with Seldon Core provides out of the box machine learning insights for monitoring, managing, scaling and debugging.

Below are some of the core components together with link to the logs that provide further insights on how to set them up.

Standard and custom metrics with prometheus	Full audit trails with ELK request logging
Explainers for Machine Learning Interpretability	Outlier and Adversarial Detectors for Monitoring
CI/CD for MLOps at Massive Scale	Distributed tracing for performance monitoring

Where to go from here

Getting Started

Seldon Core Deep Dive

Pre-Packaged Inference Servers

Language Wrappers (Production)

Python Language Wrapper [Production]

Language Wrappers (Incubating)

Ingress

Production

Advanced Inference

Examples

Reference

Developer

About the name "Seldon Core"

The name Seldon (ËSÉldÉn) Core was inspired from the Foundation Series (Sci-fi novels) where its premise consists of a mathematician called "Hari Seldon" who spends his life developing a theory of Psychohistory, a new and effective mathematical sociology which allows for the future to be predicted extremely accurate through long periods of time (across hundreds of thousands of years).

Commercial Offerings

To learn more about our commercial offerings visit https://www.seldon.io/.

License

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