Pros and Cons of Automated Machine Learning


Machine Learning is nothing but one of the subdomains of science that deals with computers or applications that are not explicitly coded to perform the task. The combination of machine learning, cognitive technology, and AI will make a lot more smooth the processing of big chunks of data and information.

Machine Learning is an application of AI (Artificial Intelligence) which ables the machines or software to adapt, learn from itself, provided the data is resourceful and sensible. Simply saying the efforts are implying to develop expert systems.

Mainly we have three categories of machine learning: Supervised Learning, unsupervised learrning, reinforcement Learning.

Since it delivers at a faster rate with better and more accurate results, machine learning is brought into practice. The engineers work day and night to predict, classify, cluster the data. The player Machine Learning is sent on the pitch of data, and Big Data to handle the problems.

Automated Machine Learning

The word automation in English means that to do work involving more than one task timely and precisely and unleashes or relieves us from mundane tasks. Machine Learning is all about making systems to know their potential to overcome these rote tasks and to enhance the power of ML, there landed up AutoML. Automated machine learning figures out the optimized techniques to the results of learning the question, ‘How?’ to accomplish those repetitive duties.

This study involves a mixture of more than one Machine Learning model into one. For classification, we may have a random forest, decision trees, or SVC (Support Vector Classification) merged into one to have better outcomes.

The whole machinery revolves around one bright star equation:

y=f(x)

With developing more models all the time, it’s becoming hectic for thinkers to choose. The choice is getting difficult, so the merging of some best-known algorithms into one is being done to achieve the goals.

ML and AutoML algorithms make the best use of Python which ultimately gives the best end results. However, Python and R, are also popular to work with Data Science as well. Creative minds can also choose Python for Data Science course for tonnes of reasons as the learning will be a big plus.

It’s a general-purpose language, can be used and dwell into any framework and makes a good match with the robust technologies (ML, AutoML, Data Science, Web Development etc.) of today whether it is to deal with data prediction, classification, and clustering.

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The problems are answered way before one bangs the door. In no time the solution is presented on your table, and most of the times the solution is garnished well.

Having acknowledged with the basics, now let me familiarize with some positives and negatives of Automated Machine Learning which come along with it.

Pros and Cons of Automated ML

Some Considerable Pros:

Some Benign Cons:

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Real World Applications of AutoML

Azure Automated Machine Learning(now GA)

The idea behind the concept is if any two data sets are found with some correlation or having similarities. For this, we require at least two data sets only then we’d be able to figure out similarities.

Google AutoML (Beta)

Unlike Azure AutoML, Google has kept enclosed in braces, it’s not an open source but huge yes to cloud-based. Has the support of few classification algorithms CNN, RNN, LSTM.

AutoKeras

It is an Open Source environment with no availability of cloud services. Supports CNN, RNN, LSTM in classification field. The technique applied in this auto model is, “Efficient Neural Architecture Search with Network Morphism.” The training framework, of course, it’s the same Keras.

The ideology of building this framework is the same as that of Google AutoML. A candidate architecture in RNN controller is trained with samples. The child model is then trained, to measure the performance of the desired tasks.

Auto-sklearn

This is open source but not cloud-based. Here regression and classification are both incorporated. The techniques used in Auto-sklearn are Bayesian optimization and automated ensemble construction. The sklearn framework keeps playing the game. The CASH definition holds the roots of this plant, i.e., Combined Algorithm Selection and Hyperparameter optimization.

The concept of Auto-sklearn is same as that of Azure Automated ML. Simultaneously, selecting a learning algorithm, along with setting its hyperparameters is considered to the problem. The major difference between both of them is that it incorporates a meta-learning step in the starting and an automated ensemble construction step at the end.

The Final Say

The nascent AutoML has just started its course. While faced with some small imperfections, I believe that they are only ephemeral and AutoML will win the game soon.

Human Wizards are in their final year at Hogwarts school of machine learning. Once graduated they will make remarkable contributions in the digital world by changing the industry norms, thus benefiting the whole mankind.

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