Exploring MLOps? Here’s a Brief Introduction

In the last few years, whether it’s about fraud detection in financial institutions, product recommendations in e-commerce websites, preventive maintenance in manufacturing and supply chain, or enabling cars with self-driving capabilities, ML-based applications have made an impact everywhere. The availability of vast volumes of data sets for training machine learning (ML) models, on-demand computing power, and increased collaboration between data scientists and developers have made ML-based applications ubiquitous. It has also made organizations realize that the processes driving the implementation of machine learning in the business space now need to be more production-oriented, instead of being just research-oriented. The need for higher efficiency, stability and end-to-end visibility and control has led to an increased interest in Machine Learning Operations or MLOps.