Auto ML
The future of Machine Learning?
So, as we know, machine learning is one of the most complex parts of Data Science. But what if there was a more time efficient way, or a more simpler way to do models, or maybe an automatic way? Well, today we’ll explore what Auto ML is.
What is it?
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning.
The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. AutoML has been used to compare the relative importance of each factor in a prediction model.
How does it compare?
In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to it.
To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. Each of these steps may be challenging, resulting in significant hurdles to using machine learning.
AutoML dramatically simplifies these steps for non-experts.
Some Libraries
Open-source
- auto-sklearn, an open-source AutoML tool implemented in Python, built around scikit-learn library
- Amazon’s AutoGluon open-source AutoML toolkit for Deep Learning, also available as AWS CloudFormation template
- TransmogrifAI, end-to-end AutoML toolkit for structured data written in Scala, that runs on Apache Spark
- Neural Network Intelligence, Microsoft’s open-source AutoML toolkit
Commercial
- AutoML Microsoft Azure cloud service
- Google Cloud AutoML solution on Google Cloud Platform
- AutoAI in IBM Watson Studio for automation of data preparation, model development, feature engineering, and hyper-parameter optimization
- Oracle Accelerated Data Science (ADS) SDK, a Python library included as part of the Oracle Cloud Infrastructure Data Science service
In the next blogs we will go in more depth into methods and explore the concept even more.
