Home Blog What Is AutoML and How Can It Boost Your Business Analytics

What Is AutoML and How Can It Boost Your Business Analytics

Published: February 4, 2022
Editor at Plat.AI
Editor: Ani Mosinyan
Reviewer at Plat.AI
Reviewer: Alek Kotolyan

Today, it is no secret that the ability of a business to remain relevant is directly proportional to its adoption of data-driven solutions.

According to Statista, our data creation and consumption in 2021 is over 200 quintillion bytes per day. Moreover, the amounts of data produced yearly, grew by 190% since 2019. In simpler words, the volume of data that our civilization generates grows exponentially.

Seizing this trend, companies increasingly rely on data for effective business analytics and decision-making. However, along with Big Data, comes the respectively big demand for data mining capacities and data professionals. The latter must be skilled enough to crunch it and extract valuable insights.

In a 2020 report from Markets and Markets, the global big data market was estimated to be $138.9 billion and projected to reach $229.4 billion by 2025 (). Meanwhile, according to LinkedIn, since 2012 the number of data science jobs increased by 650% (), and a 37% increase has been observed in 2020. There is no decline in sight for this trend, as the U.S. Bureau of Labor Statistics forecasts 28% further growth in the number of data science jobs through 2026 ().

To cover this booming demand, a growing number of higher education establishments are including data science programs in their curricula.

One of the advancements that could allow us to catch up with the pace and the volume of the virtually infinite data expansion is the automation of machine learning. Machine learning, a subset of AI, has already become indispensable for modeling and solving a range of business problems. Now Automated Machine Learning is taking it a step further, facilitating this process.

What is Automated Machine Learning and How is it Different From Traditional Machine Learning?

Automated machine learning, commonly shortened to AutoML, is essentially the automation of machine learning model development. AutoML comes in the form of either open-source libraries containing prewritten scripts of ML procedures, or platforms and software, where you interact with a user-friendly interface to get insights from your data.

Depending on the complexity of the problem, AutoML allows automation of some to nearly all the modeling steps. Manual Machine Learning is oftentimes an iterative process, where data scientists go back and forth between the steps adjusting for better results. If we visualize the process linearly, this is what it would look like:


Here, the greener the step, the higher the degree of its automation in an AutoML framework. Let’s zoom in for more understanding.

Problem definition. During this step, data scientists formulate the problem and determine the end goal of the model. It is infeasible to automate the problem formulation, as it requires a lot of domain knowledge and a flexible approach.

Data pre-processing. It is common knowledge that the quality of the model is subject to the quality of the data. Hence, data pre-processing is notorious for being the most time-consuming, labor-intensive, and important step in ML. Data pre-processing or preparation comprises data collection, cleaning, wrangling, augmentation, imputation of missing values, and exploratory data analysis.

AutoML can assist many of the tasks that are involved in data pre-preprocessing, but the degree to which the whole step can be automated is still largely dependent on the problem at hand. For instance, when managing outliers, we may not want to automatically exclude them from the dataset, as given the specific domain, they might contain interesting insights.

Feature engineering and selection. This step refers to the addition, processing, and choice of the variables that will be included in the final dataset. Some AutoML systems greatly facilitate this step by automating the feature addition and the testing of different feature combinations.

Model selection, training, and evaluation. With the conventional ML approach, the data scientist first chooses a single model and proceeds with it until the evaluation shows that it’s worth trying another algorithm.

By contrast, many AutoML systems are able to run the data through multiple algorithms simultaneously, returning a score for each option. The data analyst here is left to pick the best fit, instead of running through the loops, trying out different models.

Hyperparameter optimization. In machine learning, a parameter is the weight of a variable. It is derived from the learning process, whereas a hyperparameter is adjusted by the data scientist, to control the training process. Hyperparameter optimization is the tuning of the hyperparameters to improve the model outcomes. AutoML allows to automatically evaluate different hyperparameters to determine the set that delivers the highest-performing model.

Model Deployment. It is the final step of the model development, where the server is set up for receiving requests containing input data from the client and returning the corresponding result from the ML algorithm. In the automated version, the deployment is a one-click procedure, producing the link of the deployment service.

Monitoring. Many AutoML platforms demonstrate real-time indexes of model performance in pictorial dashboards or provide reports for the selected timeframes.

What are some examples of AutoML applications?

Just like traditional Machine Learning, AutoML can be utilized in the following industries:

• Finance & banking
• Fintech
• Investment
• Insurance
• Healthcare & biomedical
• Agriculture
• Manufacturing
• Marketing
• Retail
• Oil and Gas
• Telecommunications
• Public Sector
• Sports
• Entertainment

AutoML applications include, but are far from being limited to, the examples given below:

  • There is a multitude of ways in which AutoML can be used in the financial world: from credit-card fraud detection to various risk assessments and real-time gain and loss predictions for investments.
  • With the widespread adoption of electronic health record systems (EHR), i.e. the digitization of medical data and recordkeeping, AutoML has the potential to disrupt the medical research and healthcare industries. AutoML enables early-stage detection and diagnosis of liver and kidney ailments, diabetes, and oncology, which significantly raises the chances of successful treatment. AutoML can also be used to forecast deterioration in patients who are already ill.
  • Marketing information about customers and advertisements are analyzed by AutoML to forecast sales, maximize profits, and increase customer satisfaction.
  • In Agriculture, AutoML can analyze field satellite images to accelerate crop quality prediction and determine optimum irrigation patterns.
  • Manufacturing data can be ingested into an AutoML system to forecast delivery delays, estimate seasonal demand, control product quality, enable predictive maintenance, and minimize overall production costs.


AutoML makes AI-driven decision-making much simpler and thus more common. Consequently, AI business applications are no longer revolutionary or exclusive, but a must to keep up with the competition.

If you are thinking about implementing an AI solution for your business and want to weigh out your options: standard approach versus an AutoML software, check out “Is AutoML a good or bad idea? The complete list of pros, cons, and more. ”In this blog post, we have compiled a rundown of arguments around AutoML.

If you still have any questions or wonder which method to choose, feel free to contact Plat.AI. Our experts are there to listen to your concerns, discuss your case and help you choose the AI strategy that suits your needs best. At Plat.AI we offer both:

  • Custom modeling and deployment solutions, tailored to the needs of your business
  • An automated model building and deployment platform as a SaaS

Both of these options come with the following:

  • Easy API integrations with your current systems and teams.
  • Real-time predictions and model monitoring.
  • Straightforward and reasonable pricing.

We are quick, efficient, and secure. At Plat.AI, we work hard to enhance your team’s analytical capabilities and provide you with the best-in-class AI solutions.

Sophia Beglaryan

Sophia Beglaryan

Data AnalystSophia Beglaryan is a data analyst for Plat.AI. Sophia is an avid learner, ever curious and fascinated about a wide array of topics: from natural sciences to arts and crafts, foreign languages, and beyond. Sophia has experience translating, as well as writing, various content in English, Armenian, Russian and French.

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