The main objective of this project is to build a machine learning model using Azure Container Services.We have been provided with the banking dataset. The main steps of the project are:-

1) Authentication
2) Automated ML Experiment
3) Deploy the best model
4) Enable logging
5) Swagger Documentation
6) Consume model endpoints
7) Create and publish a pipeline
9) Documentation

An architectual diagram of the project and introduction of each step.

• We have to first register the dataset from the local files.
  • Navigate to the Datasets section in the Workspace and create a new dataset from webfile and submit the URL required for the dataset
    

• We have to build a compute instance of type DS12_V2 for running the AutoML Run.
  • Maximum number of nodes are 5 and min number of nodes are 5.

• We have to run an AutoML using the same registered Dataset.
• We have to mention the same compute instance which we build earlier.

• After running the AutoML we need to collect the best model from various diffrent models.
  
  • Here we got voting ensemble model which chooses voting model to choose the best of several runs. The base model is XGBOOST with Maxabs scaling and accuracy of 91% .

• Once we Have the best model its time to deploy the model. We can use azure Kubernetes service or azure container instane for the deployment.
• We need to choose authenticate method during the deployment method. Once deployment is succeded an endpoint will be created with status showing as healthy in workspace

• Once the model is deployed we need to enable the logs setting the appinsights = True in the Experiment logging section by adding the experiment name.
• Once we have enabled the logging we should see the status in application insights saying the failed requests, timed out requests etc.

• We can consume this endpoint using REST API or by running Azure ML python SDK's.

• Swagger is one of the API tetsing platforms available .

• Once the model is deployed we get a Swagger JSon file from the endpoint which needs to be downloaded and placed in the folder containing swagger files serve.py and swagger.sh.

• After that we need to launch a local web server using serve script and lauch swagger using docker container by running swagger.sh

• Here we are testing both using endpoint.py and Swagger

We can schedule the pipelines using schdeule recurrence parameter reducing the manual efforts.

Check out our CONTRIBUTING GUIDELINES

• Collecting more data can definitely help in improving accuracy.
• We can try testing the batch data in a schedule and see the performance.