A couple of months ago it struck me that my personal knowledge in AI could be improved quite a bit. That's why I took the time to brush up on the different concepts ranging from data gathering until the evaluation of a deployed model.
While brushing up my knowledge, I also thought on how I could share this back to the community as well as have a quick overview of everything I learned with pointers and tips of where I should look at to go more in depth on a topic. Making me end up in creating the following battle cards that I hope are useful for everyone to print and use as a kind of "Cheat Sheets".
As you might notice, there is no sklearn being utilized in steps 2. This is because I wanted to make a clear distinction between a Data Engineer and a Data Scientist, with the following reasoning:
- Data Engineer: They often utilize Spark, so we want to utilize the strength and scale-out capabilities of spark, without completely relying on the head node. Therefor I utilize Pandas examples as much as possible, which can be scaled through the Koalas platform.
- Data Scientist: They often use different libraries, with sklearn being one of them.
Note: I realize that these are far from perfect, but I want them to be. If you encounter any remarks, please post them below and I will look at incorporating them :)
- Data Gathering
- Data Cleaning, Preparation and Modification
- Model Training and Tuning
- Model Evaluation
- One Pager