Hi Codecademy team,
I have some questions regarding the general process of implementing Machine Learning. Just to recap, here are the steps of implementing ML:
- Formulate a question
- Find and Understanding the data
- Cleaning the data and feature engineering
- Choosing a model
- Tuning and Evaluating
- Using the model and presenting the results
The questions that I have regarding these non-linear implementation steps:
From step no. 5 to step no. 6, once we achieved the metrics of success in our Machine Learning Model and we have successfully presenting the results to the organisation, what do we do next ? Do we stop training our model? Or do we keep training our model using new training data?
Let’s say if we use Linear Regression Model and our coefficient of determination (R-squared) is very 0.9 (assuming this is the highest R-Squared we can achieve through the features we use in our training data), then what’s next ?
In terms of the Machine Learning Platform, is Jupyter Notebook the most common place to implement our Machine Learning Model? or is there any other platform that most organisations use these days?
It would be great to have sheds of light on these matters.
Thank you in advance,