What is a machine learning pipeline?
Many young data scientists starting their machine learning training immediately want to jump into model building and model tuning. They fail to realize that creating successful machine learning systems involves a lot more than choosing between a random forest model and a support vector machine model.
From choosing the proper ingestion mechanism to data cleansing to feature engineering, the initial steps in a machine learning pipeline are just as important as model selection. Also being able to properly measure and monitor the performance of your model in production and deciding when and how to retrain your models can be the difference between great results and mediocre outcomes. As the world changes, your input variables change, and your model must change with them.
As data science progresses, expectations get higher. Data sources become more varied, voluminous (in terms of size) and plentiful (in terms of number), and the pipelines and...
