📚 Data-Based Decision-Making: Predicting the Future Using In-Database Machine Learning

In just a few short years, machine learning (ML) has become an essential technology that companies deploy in almost every aspect of their business. Previously the preserve of giant institutions with deep pockets, the ML market is rapidly opening up. Every kind of business can now leverage ML to minimize repetitive manual processes, automate decision-making, and predict future trends.  At almost every stage of any business task, ML is making processes smarter, streamlined, and speedier. 

In recent years, technological advances have helped to democratize access and drive adoption of ML by reducing the time, skill level, and number of steps required to gain ML-driven predictions. So rapid has growth been that the global ML market is expected to expand from $21 billion in 2022 to $209 billion by 2029. Tools such as declarative ML and AutoML are helping enterprises to access powerful, business-critical predictive analytics. Taking these approaches one step further, in-database ML is a new technique that’s gaining ground. It allows businesses to easily put questions to their data and rapidly get answers back using standard SQL queries.