Files for the IBM AI Enterprise Workflow Capstone project.

Details:

• Are there unit tests for the API?

  Unittest\testAPIs.py

• Are there unit tests for the model?

  Unittest\unittest-model.py

• Are there unit tests for the logging?

  Unittest\unittest-logger.py

• Can all of the unit tests be run with a single script and do all of the unit tests pass?

  Unittest\tests-run-script.py

• Is there a mechanism to monitor performance?

  Unittest\unittest-logger.py

• Was there an attempt to isolate the read/write unit tests from production models and logs?

• Does the API work as expected? For example, can you get predictions for a specific country as well as for all countries combined?

  app.py

• Does the data ingestion exists as a function or script to facilitate automation?

  cslib.py

• Were multiple models compared?

  time-series-notebooks

• Did the EDA investigation use visualizations?

  Capstone Part1.ipynb

• Is everything containerized within a working Docker image?

  Dockerfile

• Did they use a visualization to compare their model to the baseline model?

  time-series-notebooks

• Build the Docker image and run it

  Step one: build the image (from the directory that was created with this notebook)

  ~$ cd docker

  ~$ docker build -t predict-app .

  Check that the image is there.

  ~$ docker image ls

  You may notice images that you no longer use. You may delete them with

  ~$ docker image rm IMAGE_ID_OR_NAME

  Run the container

  docker run -p 4000:8080 predict-app