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import requests, json

key = "xxxxx" # Paste your API key here
url = "https://api.bing.microsoft.com/v7.0/search"

search_term = "Azure Cognitive Services"

headers = {"Ocp-Apim-Subscription-Key" : key}
params = {"q": search_term, "textDecorations": True, "textFormat": "HTML"}

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{
    "mode": "All",
    "policyRule": {
        "if": {
            "allOf": [
                {
                    "field": "type",
                    "equals": "Microsoft.MachineLearningServices/workspaces/computes"
                },
                {

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from azureml.core import Workspace, Model
from azureml.core.model import InferenceConfig
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies

ws = Workspace.from_config()

env = Environment("inference-env")
env.docker.enabled = True
# Replace with your conda enviroment file

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name: conda-env

dependencies:
  - pip
  - pip:
    - --index-url https://xxxxx
    - --extra-index-url https://xxxxxx
    - xxxxxx==x.x.x
    - -e git+https://github.com/xxxxxxxxxxx
    - -e ./xxxxxxx

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Steps:

• Create private Feed in Azure DevOps
• Create Personal Access Token (PAT) in Azure DevOps with Feed Read permission (details)
• Navigate to the Azure DevOps Artifacts Feed page where you can see the details for the next steps (you'll need feed name, project name, organization name and later also package name):
• Create Build pipeline in Azure DevOps to create package and push to private feed:

trigger:

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# Get the HyperDriveStep of the pipeline by name (make sure only 1 exists)
hd_step_run = HyperDriveStepRun(step_run=pipeline_run.find_step_run('hd_step01')[0])

# Get RunID for best run (we're lazy)
best_run_id = hd_step_run.get_best_run_by_primary_metric().id

# Get all hyperparameters that where tried
hyperparameters = hd_step_run.get_hyperparameters()

# Get all metrics for the runs

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import os
import pandas as pd
from azureml.core import Workspace, Dataset

# Connect to Workspace and reference Dataset
ws = Workspace.from_config()
dataset = ws.datasets["german-credit-train-tutorial"]

# Create mountcontext and mount the dataset
mount_ctx = dataset.mount()  

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In train.py:

run.tag('run_type', value='training')

In later step:

#Retrieve associated run, workspace and experiment
run = Run.get_context()

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In train.py:

run.tag('run_type', value='training')

In later step:

#Retrieve associated run, workspace and experiment
run = Run.get_context()

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import json
import os
import numpy as np
import pandas as pd
import joblib

# Your imports go here
  
# Update to your model's filename
model_filename = "model.pkl"