So I do not think that this is a black/white situation.
I do agree, that any auto ml tool, cannot know what is the correct data sources for a specific use case, nor does it know the business case value of a specific model.
Auto ML should not replace humans, but work with them. I.e. it is a tool.
This tool can do the following:
1. Auto visualization.
2 Auto feature engineering.
3. Auto backtesting.
4. Auto training (with cost optimization).
5. Hyperparameter optimization.
6. Auto model packaging.
7. Autoload testing and auto security testing.
7. Auto deploy / scale / SLA.
8. Auto monitor.
The goal of AutoML is to save time and reduce risk. I am not sure why you want to do all of the above manually.
I'm all about AutoML but the best data scientists do what the author says: They try to understand the problem in detail first. I think fewer and fewer do this well.
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[ 2.6 ms ] story [ 24.5 ms ] threadI do agree, that any auto ml tool, cannot know what is the correct data sources for a specific use case, nor does it know the business case value of a specific model.
Auto ML should not replace humans, but work with them. I.e. it is a tool.
This tool can do the following:
1. Auto visualization.
2 Auto feature engineering.
3. Auto backtesting.
4. Auto training (with cost optimization).
5. Hyperparameter optimization.
6. Auto model packaging.
7. Autoload testing and auto security testing.
7. Auto deploy / scale / SLA.
8. Auto monitor.
The goal of AutoML is to save time and reduce risk. I am not sure why you want to do all of the above manually.
The goal of AutoML is to compress the time/money from idea/problem def to running models, it is not to replace the data scientist.