=================================================================================

AutoML (Automated Machine Learning) platforms, such as Google Cloud AutoML, are designed to automate the process of applying machine learning models. These platforms can select the best model, tune hyperparameters, and even handle feature selection to some extent. IT personnel, who may not be machine learning experts but have a solid technical background, can use AutoML to implement machine learning solutions efficiently. This allows them to focus more on integrating these solutions with existing IT infrastructure rather than the intricacies of model training and tuning.

One of the main benefits of using an AutoML workflow is the reduction in the time it takes to develop trained models and assess their performance. Traditional machine learning processes often involve manual and time-consuming tasks such as feature engineering, model selection, hyperparameter tuning, and performance evaluation. AutoML tools streamline and automate these tasks, allowing users to leverage machine learning without extensive expertise in the field. This automation can significantly accelerate the model development process, making it more accessible and efficient for individuals and organizations. It also helps in democratizing machine learning, enabling a broader range of professionals to harness the power of advanced analytics without spending extensive time on intricate details of the modeling pipeline. 

Vertex AI is a great platform to build some templates for machine learning, which is Google Cloud’s unified artificial intelligence platform that offers an end-to-end ML solution, from model training to model deployment. We can add a lot of data (more than 1000GB) and it's scalabe.

Figure 4113. Vertex AI providing a unified set of APIs for the ML lifecycle. [1]

Google AutoML employs a variety of techniques to handle feature selection and model optimization, although the exact specifics of these methods are not fully detailed in public documentation due to proprietary reasons. Generally, AutoML systems, including Google's, use advanced algorithms to automate much of the model-building process, including feature selection. Techniques which are typically used in AutoML for feature selection are:

• Ensemble Methods: AutoML systems often utilize ensemble techniques that combine multiple models to improve prediction accuracy. These methods inherently involve evaluating which features contribute most to predictive performance across different models.
• Regularization Techniques: Techniques like L1 (Lasso) and L2 (Ridge) regularization are commonly used in machine learning to penalize the complexity of the model. L1 regularization can particularly help in feature selection because it tends to shrink the coefficients of less important features to zero, effectively removing them from the model.
• Tree-based Methods: Decision tree-based algorithms, such as Random Forests and Gradient Boosting Machines, are frequently used in AutoML platforms. These methods are beneficial for feature selection because they provide feature importance scores based on how well individual features split the data to reduce the model's error.
• Wrapper Methods: Although more computationally intensive, wrapper methods like forward selection, backward elimination, or recursive feature elimination might be used within some AutoML frameworks to evaluate the effectiveness of subsets of features.
• Embedded Methods: These are methods where feature selection is built into the algorithm itself, such as in tree-based models or specific types of neural networks.
• Combination of these approaches. For instance, In the case of Google AutoML, it likely uses a combination of these approaches, automatically tailoring the feature selection process to the specific dataset and problem type.

============================================

[1] Diagram courtesy Henry Tappen and Brian Kobashikawa.