Introduction
We know that machine learning, deep learning are very vast topics. Learning everything is quite complicated, so scientists came up with a terminology called autoML. Automated Machine Learning (autoML) is a technology where users can create machine learning models of their own without prior knowledge of machine learning algorithms.
Also, see - Locally Weighted Regression.
Why need autoML?
As we know that skills and computing resources are two main barriers to use machine learning. The first barrier requires data scientists, which are very difficult to hire and demand hefty salaries, and the second one requires accelerated hardware(such as computers with high GPU’s). Crossing these two barriers takes lots of time as well as money.
AutoML saves us from both obstacles. AutoML makes it easier to train the model without heavy system requirements or professional skills. The user needs to provide labeled training data as input and receive an optimized model as output.
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How does autoML work?
AutoML aims to automate the entire process starting from data filtering to parameter optimization. AutoML follows the below four steps.
- Data Filtering
- Data pre-processing and feature selection
- Model selection
- Hyperparameter tuning (parameter optimization).
Till now, autoML has mainly focused on model selection and hyperparameter tuning. The brute force approach in the model selection process is to select every possible algorithm and train it using the prepared training dataset. Calculate the accuracy of every model and consider the best one. In the hyperparameter tuning process, the selected model undergoes parameter optimization where the best parameters are decided to get the best accuracy.
Advantages of autoML
1. Saves time
Choosing an appropriate model and hyperparameters for solving the problem takes a lot of time for an average person. On the first try, no one can predict the best fit model for a problem; they need to analyze data and apply different algorithms. After choosing the correct algorithm, they need to optimize the hyperparameters; finally, they will come up with the best model. AutoML does all this work on its own. The user only needs to give training data as input, and the user will get the best model. So hours of work will be done in just a couple of minutes.
2. Bridges skill gap
As we know, learning everything in Machine Learning is entirely challenging. If a person doesn't have sufficient knowledge about any algorithm, it will be challenging to implement a model. Lack of skills generates a gap between the people and the technology. AutoML fulfills this gap. It is not compulsory to know a particular algorithm; without knowing, you can implement the model without any difficulties.
3. Increases Productivity
Thinking over a problem, selecting its main features, deciding a proper algorithm for a model, and changing its hyperparameters takes lots of human effort, leading to slow production. In autoML, providing input data to the machine is enough to do all the work. It gives every information that a user wants within a couple of minutes; this leads to faster production.
4. Error Reduction
Manual implementation of the Machine Learning model can not guarantee the maximum correctness of the model so, a minimal error cannot be achieved. In the case of auto ml, all the possible causes will be taken in order to minimize the error. The model obtained as output from autoML will be of least error. In this was autoML helps in error reduction.
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