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9 Essential Steps for Selecting an Automated Machine Learning Model

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Posted May 28, 2019

Creating a machine learning model is very time-consuming, as it involves gathering the right data, cleaning it, training the algorithms, calibrating it and using the model to improve it. Automated machine learning is all about letting the computer take care of the repetitive steps and allowing humans to create and solve problems. However, there are certain features which are must-haves for a successful project in this area, as listed below.

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1. Data cleaning

A machine learning algorithm is only as good as the data used to train it. If you want to automate as much as possible, keep in mind that data processing and cleaning is one of the most time-consuming and labor-intensive processes.

Select an algorithm that can use the input in the form of the raw data as you collect it from the native source, and that output it in the way the next steps need it. This feature is highly dependent on the type of data you are working with, though. There are different ways of cleaning data if we are talking about pictures and video compared to 2text retrieved from social media.

2. Feature engineering

After data cleaning, another time-consuming process is slightly altering data to help the machine learn about variations. This can’t always be automated as it requires specific rules related to the underlying phenomenon, but most of the times generic variations work great. For example, if you are studying velocity and pressure variations, the simple rules of physics are great for approximating the results. Meanwhile, if you are testing for drug toxicity, it could be dangerous.

When selecting a system that can automatically create new features from existing data, you have to pay attention to only create those which make sense to your task at hand.

3. Algorithm library and automatic selection

An automated machine learning platform should have various features which make it useful in solving a wide range of problems. A good option should have an entire library of algorithms to choose from and the capability to add more or modify them, as required.

Most of the times selecting the best algorithm for a data set is a trial-and-error process, and you need a wide range of possible choices.

However, since you are looking to automate as much as possible, your solution should present you only with the list of appropriate algorithms for your data. Depending on the dataset, its size, and data type, the platform should already know which are the most likely tools to yield great results.

4. Automatic training

The learning process means that the algorithm goes through a couple of thousands of data points and detects patterns. The requirement you should ask for is smart hyperparametric tuning. Opposed to brute force approaches, this solution only looks of those parameters which can be improved for better fitting the model. This is important as it saves you time and computational resources which would otherwise be wasted on runs which don’t benefit the accuracy.

5. Ensembler-ready

As we’ve already said, there is no algorithm which solves every problem. Most specialists in machine learning consulting will admit that they have to use a couple of algorithms to get to an acceptable solution. Each of them will be useful on a particular segment, and you will still be left with the problem of interconnecting intermediary results.

This is where the concept of “ensembling” or “blending” comes into play. To get full automation, it is not enough to look at parts. You’ll need a platform which can connect the dots and put together the most efficient algorithms both individually and as a whole.

6. Model comparing

Machine learning has a lot of black-box parts. Due to a large number of iterations and variables, it is almost impossible to estimate which algorithm will perform better. Therefore, side-by-side testing is the only way to take the right decision. The principal axes on which such a comparison unfolds are speed and accuracy. Such a comparison doesn’t take into consideration the underlying technology. It’s only a matter of resource utilization and getting the result as fast as possible.

7. Model evaluation

As the efficiency and predictive power of a machine learning algorithm increase, so does complexity. This translates to a lack of transparency and more difficulty in understanding how it works. The downside is that if any tweaks are necessary, it is difficult to predict which type of data should be fed into the machine.

A good platform offers the opportunity to change each feature individually and observe the overall reactions of the system. Be sure to select a solution which comes with detailed documentation both for the existing features and modifying these.

8. User-friendliness and adaptability

As machine learning is used more and more by non-specialists, it needs to be set in an easy-to-understand interface, most likely a graphical one. It also needs to be easy to deploy using existing corporate setups of general data-gathering tools such as web analytics or social media scrapers. If implementing a machine learning solution required additional infrastructure investment, most likely it would be an effort which is not worth it.

9. Technical support

Regardless of how good the machine learning solution is, it always requires upgrades and maintenance. Therefore, only choose vendors who provide support around the clock and have a team of engineers and data scientists ready to help you.

These points only offer an overview of a due-diligence process for selecting an automated machine learning algorithm. Each of such algorithms needs to be considered along with additional specifications depending on the problem.

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