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Recently, I came across a great panel hosted by data mesh learning incorporation with the open-source data podcast — to discuss the significance of data discovery in data mesh architecture and other important issues surrounding data mesh delivery.
The panel consisted of expert solution architects, including Shinji Kim, CEO Select Star, Sophie Watson, Principal Data Scientist Red Hat, Mark Grover, Founder of Stemma, and Shirshanka Das, CEO Acryl Data.
Today, artificial intelligence-powered machine learning (ML) and data analytics solutions are high in demand by companies in almost every area, whether it’s the financial sector, power industry, retail, healthcare, technology, or telecommunications. ML allows companies to work through a massive amount of raw data to extract actionable information. It equips businesses to understand their targeted audience better, automate their operations and production, align customer demand, and predict future business development with reliable results to make conversant decisions.
However, implementing ML technologies and algorithms like decision trees, logistic/linear regression, KNN, etc., remained a big challenge for businesses. Given that it is a costly affair with elaborate infrastructures, subject matter experts, high computing and processing power systems, etc., to leverage ML technologies and solutions in the business infrastructure.
Machine learning is a technical self-explanatory subset of data science and an application of artificial intelligence (AI). It specializes in conditioning the machine by designing the ability to auto-learn and improve in order to execute tasks that it is not programmed to do so.
It may sound totally otherworldly but essentially it is not. A machine can actually automatically learn to perform without human intervention. Because for the most part, machine learning is about developing computer programs that enable data accession in terms of direct experience, examples, or instructions to deduce certain patterns which can be used as future reference.
