Machine learning is the most popular technology nowadays! It is currently used in almost every field that is reversible, which has pushed its importance infinitely.
What about those who don’t even know machine learning? This is where AutoML or Automated Machine Learning comes into the picture!
Automated Machine Learning (AutoML) basically automates the end-to-end process of applying machine learning to real-world problems related to reality in the industry.
In recent years, ML or machine learning has proven to be crucial to the future and has proven time and time again. It can be understood as an up and coming technology that allows different directions of research, analysis and implementation
Every ML system has multiple hyperparameters, and the most basic task in automated machine learning is to automatically set these hyperparameters to optimize maximum performance.
Especially in the deep neural networks crucially depend on a wide range of hyper parameter choices about the neural network’s architecture, regularization, and optimization. Automated hyperparameter optimization (HPO) has several important use cases; it can
- Minimize the human effort necessary for applying machine learning. This is particularly important in the context of Automatic Machine Learning.
- Improve the performance of machine learning algorithms (by tailoring them to the problem at hand); this has led to new state-of-the-art performances for important machine learning benchmarks in several studies.
- Improve the reproducibility and fairness of scientific studies. An HPO which automated is clearly more reproducible than a manual search. It enables fair comparisons since different methods can only be linked fairly if they all receive the same level of tuning for the problem at hand.
So, what is the difference between automated and traditional machine learning? Technically, the only difference is that some of the parameters used to solve it have now become learnable. That’s all. There is nothing more fanciful than that.
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At its most basic center, AutoML is able to perform a comprehensive search of hyper parameters and models. Isn’t that a brutal force? Yes. Is that a problem? Nope. Here are 3 reasons:
- In addition to considerable in-depth learning patterns, there are plenty of learning approaches that can be trained at the right time and can be trained very cheaply as you are ready to be ready.
- While you spend time creating the best custom model for your usage context, plenty of experimentation runs in the background. It provides valuable guidance on what works best and what does not. Especially at dinner or lunch. Thanks, Cloud!
- If you never want to build a simple script that is driven by multiple models and hypermeters, you may lie or you may want to rethink it and reconsider it in your daily job as a data scientist.
Building machine learning models is an experiment-based science. Not sure what works best. You need to design experiments, execute them and analyze the results.
If any experiment is expensive to implement, however, care needs to be taken not to waste resources if the results are thought to be low.
The big thing is, these days, there are still a lot of sophisticated ways to automate building machine learning models. Random and grid search has significant practical implications because they are so easy to implement;
However, they are not the most advanced learning methods. True, the search is fixed and does not learn from previous results. These days, there are better meta-learning mechanisms as well.