Machine Learning workflow

Machine Learning Workflow consists of 3 components

• Explore and process data
• Modeling
• Deployment

EXPLORE AND PROCESS DATA

This component consists of exploring and processing the data.

Retrieve

The first step is to retrieve the data, which includes test and train dataset. Lets take an example of housing dataset which contains csv files. We need to download the data from the source. 

Clean and Explore

The data might have to be cleaned to identtify and remove anomalous values such as outliers and mistakes

Prepare and transform 

• The final step in this block is to prepare and transform the data
• This steps involves normalization and converting the format of the data
• This steps involves splitting data into training, validation and test sets

MODELING

This component focuses on developing the model that is deployed to production

Develop and train model

The first step where the model is developed and trained using the training dataset.

Evaluate and model

Final step is to evaluate and validate the model. Here we tune the model using the validation data set.

DEPLOYMENT

This component focuses on deployment, monitoring and updating the model in the production environment

Deploy to production

Deployment means making the model available for use by web or software applications. We can also deploy the model to a smartphone application

Monitor and update model and data

If there is any changes in the features and data, we can update the model and data