You’ve heard about machine learning, but AutoML (automated machine learning), what is it?
Quoc Le, a pioneer in the field of AI is the man behind AutoML. And behind AutoML is the engine created by Le which is called the Neural Architecture Search. The term “AutoML” was adopted in May 2017 by Google.
Is it going to be the next wave of machine learning?
I’m sure it is, if it wasn’t for AutoML, how was it possible for the machine learning model to recognize which restaurant your bowl of noodle came from? Or how was it possible for Mercari, a Japanese based company classify images with its brand name?
Surprising, isn’t it?
Well, this is what AutoML can do.
Back to the question, Automated machine learning (AutoML) is the involvement of end-to-end automating machine learning process to the real-world problems with the help of features such as model selection, hyper-parameters tuning and preprocessing.
Was this tough to understand? Let me make it simple for you.
“AutoML is the new approach in machine learning through which the parent (neural networks) design the child neural network by totally removing humans from the picture.”
With the help of AutoML, even a person having minimal knowledge can easily train the AI system to perform many tasks without the need to know machine learning.
Tech giants such as Amazon, Microsoft, and Google are already trying to monetize AutoML with most of their B2B clients, but these researches can now be obtained online for free. If you dive deep with your research, you will be able to find multiple open source frameworks for professionals to use in their machine learning apps.
Here are a few simple basic concepts in AutoML:
For automated tools to get into competition with machine learning experts, they need to have the capability of replicating algorithms and at the same time optimize hyper-parameters like learning rates.
These are some of the toolkits that can help teach the machine the intelligence to design their very own versions.
• Auto-Sklearn – also known as out-of-the-box learning tools that seek the best machine learning dataset and use past knowledge.
• Bayesian Optimization and Hyperband (BOHB) – it is a deep learning tool that utilizes Bayesian techniques along with bandit-based methods in optimizing hyper-parameters.
•Sequential Model-Based Algorithm (SMAC) – this method uses predictive modeling that helps determine crucial input variables. It also helps optimizing algorithm parameters.
The major role an AutoML plays here is to automate repetitive tasks such as hyperparameter tuning and pipeline creation making the lives of data scientists easier.
AutoML projects to be the thriving trend in the foreseeable future for data scientists.