Example deploy to railway

.circleci/config.yml

@@ -1,4 +1,6 @@
-version: 2
+version: '2.1'
+orbs:
+  node: circleci/node@5.1.0
 
 defaults: &defaults
   docker:
@@ -21,7 +23,7 @@ prepare_tox: &prepare_tox
 
 fetch_data: &fetch_data
   run:
-    name: Set script permissions and fetch data
+    name: Fetch data
     command: |
       source venv/bin/activate
       chmod +x ./scripts/fetch_kaggle_dataset.sh
@@ -40,15 +42,19 @@ jobs:
           command: |
             tox
 
-  section_07_deploy_app_to_heroku:
+  section_07_deploy_app_to_railway:
     <<: *defaults
     steps:
       - checkout:
-          path: ~/project
+          path: ~/project/
+      - node/install:
+          node-version: '16.13'
+      - run: node --version
+      - run: npm i -g @railway/cli
       - run:
-          name: Deploy to Heroku
+          name: Deploy to Railway App (You must set RAILWAY_TOKEN env var)
           command: |
-            git subtree push --prefix section-07-ci-and-publishing/house-prices-api https://heroku:$HEROKU_API_KEY@git.heroku.com/$HEROKU_APP_NAME.git master
+            cd section-07-ci-and-publishing/house-prices-api && railway up --detach --project $RAILWAY_PROJECT_ID --environment production --service $RAILWAY_SERVICE_ID
 
   section_07_test_and_upload_regression_model:
     <<: *defaults
@@ -71,28 +77,20 @@ jobs:
             tox -e publish_model
 
 
-  section_08_deploy_app_container_in_heroku:
+  section_08_deploy_app_container_via_railway:
     <<: *defaults
     steps:
-      - setup_remote_docker:
-          # Supported versions: https://circleci.com/docs/2.0/building-docker-images/#docker-version
-          version: 20.10.6
+      - setup_remote_docker
       - checkout:
-          path: ~/project
-      - run:
-          name: Build image
-          command: |
-            sudo curl https://cli-assets.heroku.com/install.sh | sh
-            cd section-08-deploying-with-containers && make build-ml-api-heroku
-      - run:
-          name: Push Image to Heroku
-          command: |
-            # Push the built container to the Heroku image registry
-            cd section-08-deploying-with-containers && make push-ml-api-heroku
+          path: ~/project/
+      - node/install:
+          node-version: '16.13'
+      - run: node --version
+      - run: npm i -g @railway/cli
       - run:
-          name: Release to Heroku
+          name: Build and run Dockerfile (see https://docs.railway.app/deploy/dockerfiles)
           command: |
-            cd section-08-deploying-with-containers && make release-heroku
+            cd section-08-deploying-with-containers && railway up --detach --project $RAILWAY_PROJECT_ID --environment production --service $RAILWAY_SERVICE_ID
 
   test_regression_model_py37:
     docker:
@@ -265,24 +263,27 @@ tags_only: &tags_only
 
 workflows:
   version: 2
-  section_07:
+  deploy_pipeline:
     jobs:
       - section_07_test_app
-      - section_07_deploy_app_to_heroku:
+      - section_07_deploy_app_to_railway:
           requires:
             - section_07_test_app
           filters:
             branches:
               only:
                 - master
+                - demo
       # upload after git tags are created
       - section_07_test_and_upload_regression_model:
           <<: *tags_only
-      - section_08_deploy_app_container_in_heroku:
-          filters:
-            branches:
-              only:
-                - master
+
+      - section_08_deploy_app_container_via_railway:
+         filters:
+           branches:
+             only:
+               - master
+               - demo
 
 #  test-all:
 #    jobs:

README.md

@@ -2,3 +2,6 @@
 Accompanying repo for the online course Deployment of Machine Learning Models.
 
 For the documentation, visit the [course on Udemy](https://www.udemy.com/deployment-of-machine-learning-models/?couponCode=TIDREPO).
+
+
+An update.

section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb

@@ -4611,8 +4611,8 @@
    "source": [
     "# Additional Resources\n",
     "\n",
-    "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
-    "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
+    "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
+    "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
     "- [Predict house price with Feature-engine](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine) - Kaggle kernel\n",
     "- [Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python) - Kaggle kernel\n",
     "- [How I made top 0.3% on a Kaggle competition](https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition) - Kaggle kernel"

section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb

@@ -1652,7 +1652,7 @@
     "\n",
     "For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n",
     "\n",
-    "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {
@@ -1805,7 +1805,7 @@
     "\n",
     "We will do it so that we capture the monotonic relationship between the label and the target.\n",
     "\n",
-    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {
@@ -3083,9 +3083,9 @@
     "\n",
     "# Additional Resources\n",
     "\n",
-    "- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
-    "- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
-    "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8) - Article\n",
+    "- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
+    "- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
+    "- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n",
     "- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article"
    ]
   },

section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb

@@ -937,9 +937,17 @@
    "source": [
     "# Additional Resources\n",
     "\n",
-    "- [Feature Selection for Machine Learning](https://www.udemy.com/course/feature-selection-for-machine-learning/?referralCode=186501DF5D93F48C4F71) - Online Course\n",
-    "- [Feature Selection for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d) - Article"
+    "- [Feature Selection for Machine Learning](https://www.trainindata.com/p/feature-selection-for-machine-learning) - Online Course\n",
+    "- [Feature Selection in Machine Learning with Python](https://leanpub.com/feature-selection-in-machine-learning/) - Book\n",
+    "- [Feature Selection for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-selection-for-machine-learning/) - Article"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

section-04-research-and-development/06-feature-engineering-with-open-source.ipynb

@@ -2019,7 +2019,7 @@
     "\n",
     "We will do it so that we capture the monotonic relationship between the label and the target.\n",
     "\n",
-    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
+    "To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
    ]
   },
   {