By Martin Heller, contributing writer for Infoworld.com
Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data.
By Raghu Bongula, contributing writer for Forbes.com
Based on the headlines these days, it is obvious to see the rapidly emerging role that AI and machine learning play in nearly every facet of our lives. The evolution of ChatGPT has made AI a household name, and it has countless applications, from writing an article like this one to writing software code. In business, AI and machine learning harness the power of data and advanced analytics to improve efficiency by automating many tasks that would otherwise take a human much longer to accomplish.
By Mahmoud Ghorbel, contributing writer for Marktechpost.com
Data Science, a promising field that continues to attract more and more companies, is struggling to be integrated into industrialization processes. In most cases, machine learning (ML) models are implemented offline in a scientific research context. Almost 90% of the models created are never deployed in production conditions. Deployment can be defined as a process by which an ML model is integrated into an existing production environment to achieve effective data-driven business decisions. It is one of the last stages of the machine learning life cycle. Nevertheless, ML has evolved in recent years from a purely academic study area to one that may address actual business issues. However, there may be various problems and worries when using machine learning models in operational systems.
By Takis Zourntos, contributing writer for Marketscale.com
Volta Insite’s VP of Products and R&D, Takis Zourntos, reveals the game-changing potential of predictive analytics, allowing businesses to avoid unexpected equipment failures and regain control of maintenance schedules.
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