What Is Auto-ML, And What Are Its Advantages?

Automated Machine Learning, also known as Auto-ML, refers to tools and services that abstract the details and knowledge needed to perform Machine Learning (ML), automating tasks necessary for ML to occur. It is related to “no-code” and “low-code” development trends.

Generally, they cover data normalization and feature engineering steps, training models of different types and with different hyperparameters, evaluation, and comparison of results. There are also other tools, libraries, and services that help with these tasks, but with a lower level of abstraction, and so they are not called Auto-ML.

Auto-ML aims to democratize access to analytical tools for non-data scientists. It has tools that don’t need code or very little code and can help those who are already experts achieve faster results in more straightforward cases.

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Auto-ML Systems

Below we describe the main differences between Open Source and proprietary solutions and cite some examples of each.

Differences between Proprietary and Open Source Services

Proprietary Services

They have a Graphical Interface and need little or no code to perform the tasks;
They abstract infrastructure preparation, environment, and deployment;
May require an internet connection for inference;
Unavailability or latency can be issued;
Some providers have integrated annotation services (manual);
They have higher costs.

Examples: Google Cloud Platform, Azure, AWS, etc.

Open Source Tools

Free distribution;
Permission for modifications;
No restrictions on interfaces, styles, and technologies ;
No additional cost (server cost only).

Examples: Auto-Gluon, H2O, Ludwig, etc.

Advantages And Disadvantages

Some of the benefits of using Auto-ML is that it only takes superficial knowledge of ML to use the tools, automating ML steps and reducing the time to get initial results. It can be used in different types of data and problems. It can be used as a baseline for research, with the possibility of including famous techniques (e.g., Deep Neural Networks, Convolutional Neural Networks, Transformers). Some tools make it possible to customize models/layers.

However, Auto-ML may not solve the desired type of problem, having customization that can be limited or complex, or it may not get a good enough result for the pain, needing the intervention of experts. Furthermore, it will not avoid the effort to obtain and prepare data suitable for machine learning.

When Does It Pay To Use Auto-ML?

Especially when working with structured data and simple problems like regression and classification. Teams with no prior knowledge of ML are the ones who can benefit the most due to its accessibility for beginners. However, it is still essential to know the vital concepts and not eliminate efforts to obtain and analyze data.

Auto-ML has several limitations, mainly related to text, images, video, and voice processing. Another thing to consider is that Auto-ML is a relatively new field and some of the main tools like object segmentation, anomaly detection, forecasting, among others, are not yet fully developed.

Machine Learning: Benefits For Companies

Undoubtedly, its use in companies is very advantageous and, therefore, increases the demand in the area. We can highlight some advantages of Machine Learning. Among them are:

1. Promotes Customer Loyalty

According to a survey conducted by an American company, Salesforce, there is a high probability that the customer will switch brands if it can no longer meet their needs. Machine Learning ensures that the company can anticipate this customer’s needs by collecting data in this situation. With this, the company can provide a personalized experience since their experiences are precisely localized. This advantage avoids service problems and makes it possible to identify potential customers more quickly and efficiently.

2. Positively Influences Decision Making

The information obtained through Machine Learning positively influences a company’s strategic planning. This is because the machine can quickly analyze large volumes of data, allowing doubts to be clarified for decision-making. It is not only large companies that benefit from this Artificial Intelligence. For example, any company that has digitized information, such as on a website, social media, or an electronic store, can use Machine Learning for data analysis. A small business has more data than you might think. Using Machine Learning, it is easier to collect data that promote good results and obtain precious information for future strategies.

3.Speed Processes Automatically.

Every company aims to optimize its processes to guarantee the quality and efficiency of the services provided to the customer. This becomes even more important when the company has a minimum number of employees. The fewer people working, every saved hour of work is a big plus. Everyday tasks like emailing billing or appointment reminders, generating leads, and being present on social media can all be automated. The machine is automatically responsible for scheduling the next steps of the processes and can perform these simple tasks. Machine Learning can do it instead of the employee spending a lot of time performing these activities.

4.Strengthens Your Company’s Digital Security

We know the importance of protecting our data due to the excessive number of cases of cyber-invasions. Because of these threats, we need to carefully manage a business’s data to maintain the brand’s reputation. Digital monitoring can cost time and money, but Machine Learning allows for the immediate identification of activities deemed suspicious. In addition to detecting any attack, the use of ML protects any company at all times.

Also Read: Different Forms Of Machine Learning