Exploring MLflow use cases with AutoML
Executing an ML project requires a breadth of knowledge in multiple areas and, in a lot of cases, deep technical steps of expertise. One emergent technique to ease the adoption and accelerate time to market (TTM) in projects is the use of automated machine learning (AutoML), where some of the activities of the model developer are automated. It basically consists of automating steps in ML in a twofold approach, outlined as follows:
- Feature selection: Using optimization techniques (for example, Bayesian techniques) to select the best features as input to a model
- Modeling: Automatically identifying a set of models to use by testing multiple algorithms using hyperparameter optimization techniques
We will explore the integration of MLflow with an ML library called PyCaret (https://pycaret.org/) that allows us to leverage its AutoML techniques and log the process in MLflow so that you can automatically obtain the best performance...
