AutoML Unlikely to Kill the Demand for AI Developers

Machine learning is the emerging future technology. Artificial intelligence entailed with machine learning increases the demand for machine learning engineers and data scientists. But handling machine learning is a tough job.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning educates itself with its sense to observe. It doesn’t need a human hand for help. Whether it is an action or a task, machine learning observes it closely and tries to imitate the function by putting the actions in its system.

Machine learning is a popular technology that is being used in various sectors. It is in use at almost all the fields. Data scientists and machine learning engineers are used to functioning with the technology. But what about people who don’t know machine learning? There is an emerging solution for them. Automated machine learning or AutoML comes for their aid.

What is AutoML?

AutoML or automated machine learning involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry. The aim is to reduce or eliminate the need for skilled data scientists to build machine learning and deep learning models. An AutoML system allows anyone to provide the labelled training data as input and receive an optimized model as output.

In recent years, machine learning has been noticed as the key to the future. However, handling and programming machine learning involves various directions of research, analysis and implementation. By the technical process, machine learning is confined to data scientists and machine learning enthusiasts and researched. To break the chain, AutoML bridges the gap that provides a theory or concept of automated machine learning.

A data scientist has to apply the appropriate data pre-processing, parameter engineering, parameter extraction and parameter selection methods that make the datasets ready to configure. And later it involves algorithms to get a final machine learning model. AutoML remedy was provided to challenge the lengthy methods. It can apply machine learning without such expertise or an ML expert.

Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.