In-depth Analysis of AutoML

Running on its own, automated machine learning algorithms are the latest thing that digital transformation age offered. This smooth observation will point out the value proposition of the AutoML products in general.

Machine Learning’s propulsion in business intelligence applications is something to note for. In conventional statistics, table values and data sets are auxiliary things that can be referred to before or after the main job is done. However, machine learning proposes the ability to get planning before, see projections during, and take result statistics after the job. That is, machine learning can be integrated into the system so that observers would have the ability of controlling and following the job and its future projections.

In the paradigm of statistical controlling, the idea behind the machine learning has been common for decades. With other mathematical tools and methods, main aim was to find correlation, and if there is any, next step was to find the magnitude of correlation. That is, how a certain parameter behaves, when another parameter is changed providing that to keep all other parameters constant. If a statistically significant correlation is found, the magnitude of that correlation tells about the scale of the change of the parameter. For example, an individual’s consumption function consists of individual’s wealth, propensity to save (MPS) and propensity to consume (MPC). There is a correlation between independent variables wealth, MPS, and MPC and dependent variable consumption as a change in the independent variables will alter the dependent one. Moreover, the magnitudes of multipliers have an effect too. Propensity to save has a negative effect on consumption while propensity to consume and wealth have a positive effect. Thus, the idea behind finding individual’s consumption pattern lies before the correlation between parameters. Although this was the oldest methods, machine learning algorithms are using same logic with slightly different methods to find correlations and hidden patterns between parameters.

Hence, utilizing machine learning not only accelerates the revealing correlations but it also helps to change the business intelligence methods of the past. The key aspects of machine learning here is that above example of traditional computing we have collected the data, constructed a program; hence, computer gave us an output to observe. However, in the case of machine learning we collect data, and we feed the computer with former outputs so that it gives a program (model) to explain the relation between data and outputs. Although machine learning algorithms are a small part of the real practices, the algorithmic process looks like this:

1. Comprehend the Domain, Information, And Aims
2. Pre-Process the Data (I.E. Select Clean, Integrate)
3. Comply Learning Models
4. Interpret the Results
5. Merge the Prior Information with New Ones
6. Start Again.

This cycle goes in loop until a viable result is found from it. However, as can be seen following the best practice in machine learning is time consuming although it is important. This is the place where automation comes in. In our opinion there are some things that computers can do better and some things that humans can do better. Humans are best at communication and creativity whereas computers are best at repetitive mundane tasks. Thus, freeing up humans from mundane tasks will benefit the process as technology can help in that regard. All steps above can be automated with a few additions to get the full out of it. So, why not use AutoML while you can? There are so many benefits of utilizing AutoML that can be seen from its algorithmic process:

1. Preprocessing the Data
2. Construct Features
3. Choose from Diverse Algorithms
4. The Best Algorithm Selection
5. Training and Fine Tuning the Algorithm
6. Ensemble the Algorithms to Form a Blend
7. Choose Best Model by Model Competitions
8. Document the Insight into Human-Interpretable Ones
9. Make it Ready for Easy Deployment
10. Model Management

Enhencer makes sure that everybody wanting to implement machine learning to their business can do without the hassle of entry barriers. Enhencer’s state-of-art machine learning platform guides every user to get the most out of ML. As an MLaaS platform offering AutoML power, Enhencer will help to smarten your business and to reach digital transformation era without even lifting a second finger.