trainindata · Samsontontoye · Feb 20, 2023 · Feb 20, 2023 · Feb 21, 2023 · Mar 1, 2023
diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -1,4 +1,6 @@
-version: 2
+version: '2.1'
+orbs:
+  node: circleci/[email protected]
 
 defaults: &defaults
   docker:
@@ -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:[email protected]/$HEROKU_APP_NAME.git master
+            cd section-07-ci-and-publishing/house-prices-api && railway up --detach
 
   section_07_test_and_upload_regression_model:
     <<: *defaults
@@ -71,28 +77,22 @@ 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
+          version: 20.10.18
       - 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
 
   test_regression_model_py37:
     docker:
@@ -265,24 +265,27 @@ 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:

diff --git a/README.md b/README.md
@@ -2,3 +2,5 @@
 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 Readme.
diff --git a/section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb b/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"

diff --git a/section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb b/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"
    ]
   },

diff --git a/section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb b/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": {

diff --git a/...learning-pPipeline-scoring-new-data.ipynb → ...-learning-pipeline-scoring-new-data.ipynb b/...learning-pPipeline-scoring-new-data.ipynb → ...-learning-pipeline-scoring-new-data.ipynb
diff --git a/section-04-research-and-development/06-feature-engineering-with-open-source.ipynb b/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)."
    ]
   },
   {

diff --git a/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb b/section-04-research-and-development/08-final-machine-learning-pipeline.ipynb
@@ -1081,7 +1081,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1095,7 +1095,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.5"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/section-05-production-model-package/regression_model/pipeline.py b/section-05-production-model-package/regression_model/pipeline.py
@@ -1,9 +1,5 @@
 from feature_engine.encoding import OrdinalEncoder, RareLabelEncoder
-from feature_engine.imputation import (
-    AddMissingIndicator,
-    CategoricalImputer,
-    MeanMedianImputer,
-)
+from feature_engine.imputation import AddMissingIndicator, CategoricalImputer, MeanMedianImputer
 from feature_engine.selection import DropFeatures
 from feature_engine.transformation import LogTransformer
 from feature_engine.wrappers import SklearnTransformerWrapper

diff --git a/section-05-production-model-package/requirements/requirements.txt b/section-05-production-model-package/requirements/requirements.txt
@@ -7,5 +7,5 @@ pydantic>=1.8.1,<2.0.0
 scikit-learn>=1.1.3,<2.0.0
 strictyaml>=1.3.2,<2.0.0
 ruamel.yaml>=0.16.12,<1.0.0
-feature-engine>=1.0.2,<2.0.0
+feature-engine>=1.0.2,<1.6.0  # breaking change in v1.6.0
 joblib>=1.0.1,<2.0.0
diff --git a/section-05-production-model-package/tox.ini b/section-05-production-model-package/tox.ini
@@ -15,7 +15,7 @@ skipsdist = True
 [testenv]
 basepython = python
 install_command = pip install {opts} {packages}
-whitelist_externals = train
+allowlist_externals = train
 
 setenv =
 	PYTHONPATH=.
@@ -47,7 +47,6 @@ deps =
 commands =
 	flake8 regression_model tests
 	isort regression_model tests
-	black regression_model tests
 	{posargs:mypy regression_model}
 
 

diff --git a/section-06-model-serving-api/house-prices-api/requirements.txt b/section-06-model-serving-api/house-prices-api/requirements.txt
@@ -5,4 +5,5 @@ pydantic>=1.10.4,<1.12.0
 typing_extensions>=4.2.0,<5.0.0
 loguru>=0.5.3,<1.0.0
 # We will explain this in the course
-tid-regression-model>=3.2.0
+tid-regression-model>=3.2.0
+feature-engine>=1.0.2,<1.6.0  # breaking change in v1.6.0
diff --git a/section-06-model-serving-api/house-prices-api/runtime.txt b/section-06-model-serving-api/house-prices-api/runtime.txt
diff --git a/section-07-ci-and-publishing/gemfurypushurl.txt b/section-07-ci-and-publishing/gemfurypushurl.txt
@@ -0,0 +1 @@
+gemfury_push_url - https://gxpw8hV5RQwZ8qY9s69t:@pypi.fury.io/samsontontoye/
diff --git a/section-07-ci-and-publishing/house-prices-api/app/__init__.py b/section-07-ci-and-publishing/house-prices-api/app/__init__.py
@@ -1 +1 @@
-__version__ = "0.0.2"
+__version__ = "0.0.5"
diff --git a/section-07-ci-and-publishing/house-prices-api/requirements.txt b/section-07-ci-and-publishing/house-prices-api/requirements.txt
@@ -5,4 +5,5 @@ pydantic>=1.10.4,<1.12.0
 typing_extensions>=4.2.0,<5.0.0
 loguru>=0.5.3,<1.0.0
 # We will explain this in the course
-tid-regression-model>=3.2.0
+tid-regression-model>=3.2.0
+feature-engine>=1.0.2,<1.6.0  # breaking change in v1.6.0
diff --git a/section-07-ci-and-publishing/house-prices-api/runtime.txt b/section-07-ci-and-publishing/house-prices-api/runtime.txt
diff --git a/section-07-ci-and-publishing/model-package/publish_model.sh b/section-07-ci-and-publishing/model-package/publish_model.sh
@@ -2,7 +2,7 @@
 
 # Building packages and uploading them to a Gemfury repository
 
-GEMFURY_URL=$GEMFURY_PUSH_URL
+GEMFURY_URL=https://gxpw8hV5RQwZ8qY9s69t:@pypi.fury.io/samsontontoye/
 
 set -e
 
@@ -41,4 +41,4 @@ else
     ls -d */ | while read dir; do
         build $dir
     done
-fi
+fi
diff --git a/section-07-ci-and-publishing/model-package/regression_model/VERSION b/section-07-ci-and-publishing/model-package/regression_model/VERSION
@@ -1 +1 @@
-4.0.3
+4.0.5
diff --git a/section-07-ci-and-publishing/model-package/requirements/requirements.txt b/section-07-ci-and-publishing/model-package/requirements/requirements.txt
@@ -7,5 +7,5 @@ pydantic>=1.8.1,<2.0.0
 scikit-learn>=1.1.3,<2.0.0
 strictyaml>=1.3.2,<2.0.0
 ruamel.yaml>=0.16.12,<1.0.0
-feature-engine>=1.0.2,<2.0.0
+feature-engine>=1.0.2,<1.6.0  # breaking change in v1.6.0
 joblib>=1.0.1,<2.0.0
diff --git a/section-07-ci-and-publishing/model-package/tox.ini b/section-07-ci-and-publishing/model-package/tox.ini
@@ -15,14 +15,15 @@ skipsdist = True
 [testenv]
 basepython = python
 install_command = pip install {opts} {packages}
-whitelist_externals = train
+allowlist_externals = train,python
 
 passenv =
-	KAGGLE_USERNAME
-	KAGGLE_KEY
-	GEMFURY_PUSH_URL
+	godfatherfigure
+	ecd29195e37703773256fad8d107c3f2
+	https://gxpw8hV5RQwZ8qY9s69t:@pypi.fury.io/samsontontoye/
 
 [testenv:test_package]
+allowlist_externals = python
 deps =
 	-rrequirements/test_requirements.txt
 
@@ -50,8 +51,9 @@ commands=
 
 [testenv:fetch_data]
 envdir = {toxworkdir}/test_package
+allowlist_externals = unzip
 deps =
-	{[testenv:test_package]deps}
+	kaggle<1.6.0
 
 setenv =
 	{[testenv:test_package]setenv}
@@ -65,6 +67,7 @@ commands=
 
 [testenv:publish_model]
 envdir = {toxworkdir}/test_package
+allowlist_externals = *
 deps =
 	{[testenv:test_package]deps}
 
@@ -89,4 +92,4 @@ commands =
 
 [flake8]
 exclude = .git,env
-max-line-length = 90
+max-line-length = 90
diff --git a/section-08-deploying-with-containers/Makefile b/section-08-deploying-with-containers/Makefile
diff --git a/section-08-deploying-with-containers/house-prices-api/requirements.txt b/section-08-deploying-with-containers/house-prices-api/requirements.txt
@@ -7,4 +7,5 @@ pydantic>=1.10.4,<1.12.0
 typing_extensions>=4.2.0,<5.0.0
 loguru>=0.5.3,<1.0.0
 # fetched from gemfury
-tid-regression-model==4.0.2
+tid-regression-model==4.0.5
+feature-engine>=1.0.2,<1.6.0  # breaking change in v1.6.0
diff --git a/section-08-deploying-with-containers/house-prices-api/runtime.txt b/section-08-deploying-with-containers/house-prices-api/runtime.txt
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2,3 +2,5 @@
		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 Readme.
-Original file line number
+Diff line change
@@ Expand Up / @@ -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)."
        ]
       },
       {
@@ Expand Down @@
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
		gemfury_push_url - https://gxpw8hV5RQwZ8qY9s69t:@pypi.fury.io/samsontontoye/