What is Automated Machine Learning?
It is the term used for the technology automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning problem requires a dataset that consists of some input data on which a training model is needed to be built. The input data may not be in such a form that all machine learning algorithms may be applied to it. An ML expert needs to implement the appropriate procedures (including data pre-processing steps, feature scaling, feature extraction), resulting in a dataset suitable for machine learning. Building the model involves the selection of the best algorithm for maximizing performance from the dataset. Many of these steps are often beyond the abilities of non-experts. Considering this in mind, AutoML was proposed as an Artificial Intelligence-based solution to the gruesome challenge of applying machine learning. AutoML in machine learning using python and r thus started gaining popularity. (Read – AI and ML. Are they one and the same?)
What is the Need for AutoML?
The idea of AutoML took off with the development in the field of Artificial Intelligence. It all took shape when Jeff Dean, Google’s Head of AI, suggested that “100x computational power could replace the need for machine learning expertise”. This raised several questions:
Do hundreds of thousands of developers need to “design new neural nets for their particular needs,” or is there an effective way for Neural Networks to generalize similar problems? Or can a large amount of computation power replace machine learning expertise?
Clearly, the answer is NO. Many factors support the idea of AutoML:
- Shortage of machine learning expertise
- Machine-Learning expertise is cost-inefficient
For large organizations requiring high efficiency, AutoML cannot replace a machine learning expert, but it can be cost-effective and can be useful for smaller organizations.
Applications of AutoML
AutoML can be used for the following tasks using AutoML platforms like Google cloud AutoML:
- Automated Data Preparation
It Involves column type detection, intent detection, and automated task detection within the dataset.
- Feature Engineering
It includes Feature Scaling, meta-learning, and feature selection.
- Automated Model Selection
AutoML can help in model selection.
- Automated problem checking
Problem checking and debugging can be automated.
- Automated analysis of results obtained
Applying wonders of AI can save time and capital.
Here is a good read – Two Real Life Examples of Google’s Automated Machine Learning.
Popular AutoML Libraries like Featuretools, Auto-sklearn, MLBox, TPOT, H2O, Auto-Keras are the ones contributing to enhanced AutoML experience.
Advantages of AutoML
- The installation of the libraries is effortless.
- The introduction of Cloud AutoML has speeded up the development of AutoML.
- Cost-effective, and Labour-efficient.
- Require a lower level of expertise.
Limitations of AutoML
Although coming with a set of advantages, advanced AutoML introduces the concept of hyperparameters, which are itself needed to be learnt. AutoML can be usefully incorporated for doing a task that can be generalized, but for functions that are unique and require some level of expertise, AutoML turns out to be a disaster.
Future of AutoML
Automated Machine Learning (AutoML) has been gaining traction within the Data Science community. This surge of interest is reflected in the development and release of numerous open-source Automated Machine Learning tools and libraries, which are mentioned above, and on the emergence of businesses focused on building and commercializing AutoML systems (like DataRobot, DarwinAI, H2O.ai, OneClick.ai). AutoML is a hot topic for the industry, but it is not all-set for replacing data scientists from existence. Besides the difficulty of automating many of the data science tasks, its sole purpose is to assist data scientists and free them from the burden of repetitive, and less demanding jobs that can be generalized, so they can invest their time on tasks that are more challenging, creative, and harder to automate. (AutoML: The Next Wave of Machine Learning)
Concluding, we live in an era where the growth of data beats our ability to make sense of it. AutoML is an exciting technological field that has been in the spotlight and which promises to mitigate this problem through the development in the sector of Artificial Intelligence.
We expect significant strides of progress in this field in the near future, and we recognize the help of AutoML systems in solving many of the challenges that we face out there.