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Examples for deployment of ML models on K8s via Seldon Core. Order according from simple to sohpisticated.

    1. 01_getting-started: deploys iris model binary provided by Seldon on Google Cloud Storage with pre-packaged seldon sklearn MLServer.
    1. 02_getting-started-custom: Trains iris model, use custom language wrapper (predict class) and build image with s2i, use with MLServer.
    1. 03_getting-started-custom2: as example 2_getting-started-custom but build image with regular docker file instead of s2i.
    1. 04_getting-started-custom-mlflow/: Trains iris model and saves to mlflow. Loads separate Mlflow Cloud Storage model as deploys with pre-packaged seldon mlflow MLServer.
    1. 05_inference-graph-randab: Demos random A/B test with two models leveraging the seldon RANDOM_ABTEST implementation.
    1. 06_canary-rollout: Demos the application of two models via a Canary deployment.
    1. 07_seldon-grafana-metrics: Simple deployment with notes about working with Prometheus/Grafana.
    1. 08_sklearnserver: effort to build custom seldon inference server. Not pursued further as implementation requires modification of seldon core operator settings, which is unpractical for deployment at this point.
    1. 09_text_classifier_mlflow: trainings NLP classifier model with some dependencies and saves as custom Python mlflow model. then deployment via pre-packaged seldon mlflow server.
    1. 10_translator_mlflow: deploys Transformers French-to-English language translator model. Model mlflow artifact is pulled from minio on k8s.