FarrandTom, IBM’s Tom Farrand has put together a great browser based tutorial for Seldon Core on Katacoda where users can install and then deploy an instance of the SKLearn Iris classifier.
In the scenario, users will deploy Seldon Core to a Kubernetes environment and then use Seldon Core to deploy and test a machine learning classifier pre-trained on the Scikit-Learn Iris dataset.
The tutorial is designed to take an estimated 20 minutes – get started on Katacoda now!
What is Seldon Core?
Seldon Core is an open source platform to deploy your machine learning models at scale on Kubernetes.
Seldon Core makes it simple to convert machine learning models into production-grade microservices following three steps:
Containerise: Model binaries from popular frameworks (Scikit-Learn, Tensorflow, XGBoost) are readily containerised thanks to pre-built model servers. Custom models are supported using language wrappers (Python, Java) allowing you to take any model in these languages and containerise them.
Deploy: Seldon Core extends Kubernetes by adding the custom SeldonDeployment resource. Seldon deployments support a range of complex inference patterns, such as canary rollouts and multi-armed bandits. Seldon deployments are built out of a number of core components e.g. Transformers, Predictors, Explainers, Routers, etc.
Monitor: Logging can easily be configured to support tracing of network traffic to deployments, monitoring of request/response payloads, visualisation of real time model health. Alerting for outlier detection and concept drift can be setup.
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