If you have already read some machine learning books. You have noticed that there are different ways to stream data into machine learning.
Most of these books share the following steps:
- Define Problem
- Specify Inputs & Outputs
- Exploratory Data Analysis
- Data Collection
- Data Preprocessing
- Data Cleaning
- Visualization
- Model Design, Training, and Offline Evaluation
- Model Deployment, Online Evaluation, and Monitoring
- Model Maintenance, Diagnosis, and Retraining Of course, the same solution can not be provided for all problems, so the best way is to create a general framework and adapt it to new problem.
You can see my workflow in the below image :