Knowledge of statistical methods, strong programming skills, excellent communication skills, and an understanding of economic relationships: Good data scientists combine these skills to generate added value for companies from data. However, the availability of good data scientists in the labor market is limited, and their salary needs to be higher. At the same time, companies increasingly want to tap the potential of data and generate digitally competitive advantages with machine learning. AutoML, i.e., automated machine learning, is intended to enable more companies to use machine learning.
What Is The Background Of AutoML?
Machine learning stands for the extraction of knowledge from data. To do this, machine learning models learn patterns in the data and can provide forecasts based on them. The path from the raw data to a reliable forecast includes a series of work steps. Data preparation, feature engineering, model selection, optimization, and provision of results: typically, the individual process steps are driven forward by a team of data experts (data scientists, data engineers, etc.). The goal of AutoML tools is to automate this process. The focus is on training the analysis models.
What Are The Benefits Of AutoML?
The advantages of successfully automating the machine learning process are apparent:
- Cost reduction: The need for skilled workers is reduced due to the elimination of manual work steps. Even companies with a smaller budget can actively tackle the topic of machine learning.
- Increased efficiency: Companies can generate results faster with AutoML.
- Scalability: Even users without programming and ML knowledge can more easily benefit from the knowledge gained from machine learning. Many AutoML tools have graphical user interfaces for ease of use.
- Support: AutoML can also support existing data science units in companies; for example, an AutoML model started at the push of a button can be the starting point for more in-depth analysis steps.
Will AutoML Replace The Data Experts?
No, because AutoML tools alone only bring limited added value to companies. To make decision-making more data-based, experts are needed to help get ML results to the company in a meaningful and understandable way. The data scientist is responsible for bridging the gap between the ML models’ output and the company’s business requirements. How can the results be interpreted? How do they come about? What impact do they have?
Human experts’ know-how, experience, and communication skills help to understand the automatically generated results and create acceptance for data-driven changes. The AutoML providers themselves have also recognized the problem of insufficient interpretability and are trying to expand their range here in particular, Especially with complex analysis projects and poorly prepared data, AutoML reaches its limits, and the expertise of data scientists and data engineers comes into play. Mainly when used in sensitive business processes, the AutoML also needs to be monitored by experts.
What AutoML Tools Are There?
There is a wide range of AutoML solutions to choose from. In addition to the offers of the major providers, such as Google (Google Cloud AutoML) or Amazon (SageMaker Autopilot), there are several other tools. The recommended H2O AutoML, TPOT, and AutoFolio are worth mentioning here. In another article, we present our YUNA elements solution for the convenient administration, execution, and monitoring of AutoML models. AutoML helps make machine learning more mainstream.
The hurdles of know-how and financial investment become more accessible for companies to overcome with AutoML. However, human expertise is required for the successful, productive use of machine learning, especially in complex fields of application. AutoML can generate added value in a few clearly defined areas. In addition, AutoML and data scientists need to be connected. Here, too, AutoML helps to make the machine learning process more efficient and faster because it can scale the work of the data scientists.
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