What is Automated Machine Learning (AutoML) & Its Benefits For Business
This article discusses- What is automated machine learning? How does it help businesses in retail, manufacturing, healthcare, and transportation in developing a machine learning model?
What is automated machine learning?
Automated machine learning is the automation of ML algorithms and structured design process of a defined model. It provides predesigned systematically structured data analysis tools that help industries like retail, transformation, healthcare, etc. in obtaining the best machine learning algorithms practices for accurate predictions with low cost and quality time.
With the automated ML, a company can derive the same results in less time at a low cost. As the datasets of different algorithms applications used and tested by various data scientists are coded and recorded previously, it provides a pre-designed data analysis structure, that helps in applying the right algorithms with perfect tuning framed settings that reduces data scientists’ quality time in providing accurate results.
Let us consider an example for easier understanding of automated machine learning- Sau, a company that plans to use machine learning algorithms for predicting sales reports for the current year using the last few years of data. As with traditional ML models, the datasets needed to test various algorithms with different tuning settings to make accurate results, which may take long periods and huge investments. Automated machine learning is a fundamental shift of how all sizes of businesses use, develop and implement machine learning algorithms that drive growth. With the feasibility of using predefined systems, the work can be completed within less time.
Automated machine learning benefits
- It is considered that data scientists spend 60% of their time cleaning and organizing data sets and 19% in collecting data sets. This reduces the quality of time that they spend in solving critical problems. Automated machine learning changes the making and use of machine learning models with ease and with the predeveloped systems so that the data scientists in the organization can focus more on complex problems.
- In building a machine learning model, the data scientist follows sequential traditional steps, like collecting raw data, analyzing and filter raw data, selecting an algorithm that helps in solving the problem, training and tunning the algorithm, testing the algorithm function for acquiring results, and repeat the process until they find the best algorithm.
As there is no best algorithm for solving a problem, the data science team needs to figure out the right algorithms using feasible data. If the data scientists are untrained or unaware of assigned task-related problem-solving techniques, they need to communicate with various people like developers, designers, and management. This is time-consuming and cost-intensive. It can be solved using AUTOML.
Automated Machine Learning Example
Japan’s largest credit card company SSMC, has applied AutoML in risk modeling and customer insight/marketing applications. After observing some of the analysts and data scientists practicing machine learning manually, for the risk modeling process, and they are taking half a year to build and validate a model. Hiroki Shiraishi, team lead of the machine learning infrastructure team at SMCC’s business units, noted that the company wanted to escalate the process of analyzing credit card data, and there were not enough skilled analysts to meet the need. Therefore, the use of AutoML can cut the time to hours or a few days. By increasing modeling productivity.
AutoML Tools That Are Automating The Machine Learning Process
- Amazon Alex is the same deep learning technology that built Amazon Alexa. It helps in building advanced chatbots in less time, that can respond to human conversations by increasing user interface.
- Auto Folio an algorithm selection tool, which helps developers in selecting the right algorithm with accurate hyperparameters, with the best selection tools. Auto folio saves time and helps in getting accurate results with the use of the best algorithm for instance. To analyze which algorithm choices are best, AutoFolio uses two complementary methods for assessing parameter importance in algorithm configuration spaces, the functional ANOVA for a global measure of parameter importance and ablation analysis for a local measure.
- Auto scklearn is the software developed by the University of Freiberg, which has unlimited availability of machine learning algorithms. This helps in selecting the right algorithm with accurate hyperparameters that provides the best results. Auto-scklearn provides out-of-the-box supervised machine learning techniques to scientists.
Automated machine learning software is enhancing the workflow of analysts and data scientists, rapidly increasing the speed of testing different algorithms and hyperparameters that provides the best route to solve the problem for accurate results.
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