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kind: AuthorizationPolicy
apiVersion: security.istio.io/v1beta1
metadata:
  name: allow-to-example-model
  namespace: example-model
spec:
  action: ALLOW
  rules:
    - when:
        - key: "request.auth.claims[email]"

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kind: RequestAuthentication
apiVersion: security.istio.io/v1beta1
metadata:
  name: enforce-jwt-for-apis
  namespace: api
spec:
  jwtRules:
    - audiences:
        - ml
      fromHeaders:

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- name: ISTIO_ENABLED
  value: 'true'
- name: ISTIO_GATEWAY
  value: api/seldon-gateway
- name: ISTIO_TLS_MODE
  value: STRICT

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apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: example
  namespace: example-model
spec:
  name: example
  predictors:
    - componentSpecs:
        - spec:

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from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
import bentoml
from bentoml.adapters import DataframeInput
from bentoml.frameworks.sklearn import SklearnModelArtifact

def train():
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

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import joblib
from flask import Flask, request
import numpy as np
import os


app = Flask(__name__)
model = None

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apiVersion: batch/v1
kind: Job
metadata:
  name: training-job
spec:
  template:
    spec:
      containers:
      - name: train-model
        image: bdrci/k8s-model-trainer

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from sklearn import datasets
from sklearn import svm
import joblib


def train(data):
    clf = svm.SVC(gamma=0.001, C=100.)
    clf.fit(data.data[:-1], data.target[:-1])

    return clf

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# data generation functions
def normalize(x):
    mu = np.mean(x)
    sigma = np.std(x)
    return np.array([(elem - mu)/float(sigma) for elem in x])

def generate_data(n_obs, changepoint_time):    
    # Features
    X =  pd.DataFrame({'day': np.arange(1, n_obs + 1),
                       'weight_kg': np.random.normal(5000, 500, n_obs),

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Snippet deleted due to copyright violation