Finding the optimal model with Azure Automated Machine Learning
Automated machine learning is an exciting new trend that many (if not all) cloud providers follow. The aim is to provide a service to users that automatically preprocesses your data, selects an ML model, and trains and optimizes the model to optimally fit your training data given a specific error metric. In this way, it will create and train a fully automated end-to-end ML pipeline that only needs your labeled training data as input. Here is a list of steps that Azure Automated Machine Learning optimizes for you:
- Data preprocessing
- Feature engineering
- Model selection
- Hyperparameter tuning
- Model ensembling
While most experienced machine learning engineers or data scientists would be very cautious about the effectiveness of such an automated approach, it still has a ton of benefits, which will be explained in this section. If you like the idea of hyperparameter tuning, then you will definitely...
