Operationalizing machine learning is a critical step in making AI-powered products and services successful. Let's discuss how MLOps can help businesses resolve issues efficiently. Operationalizing machine learning, or "MLOps", as it is now called, is the latest trend in many industries. Operating is something that businesses do every day; they operate their factories, their offices, their stores, and so on. But what does it mean to "operationalize machine learning"?

Machine learning (ML) and artificial intelligence (AI) have become two fast-track routes to innovation across businesses. But too often, they're implemented ad hoc, or too slowly--a result of developing AI from scratch within small sections of their organization. Instead, instilling a framework of machine learning operations within your organization is a key way to more holistically integrate the use of AI and machine learning into your day-to-day business operations. "AI maturity is not optional," says Kia Javanmardian, senior partner at QuantumBlack, McKinsey & Company's AI arm, which helps companies unlock performance through operational ML capabilities. "It is correlated with returns."

The data science and machine learning platform space is dynamic and crowded. In addition to understanding what the market has to offer, shopping for a platform means assessing the needs of your data science, IT, and leadership teams.

Today's world has recently taken up an increased focus on machine learning and with data scientists/data miners/ predictive modellers / *whatever new job term may emerge* operating at the cutting-edge of technology, it cannot be forgotten that machine learning needs to be implemented in such a way to aid in the solution of real business problems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones). The reason why these are important is that these issues affect the statistical properties of the datasets and interfere with the assumptions made by algorithms when run against these dirty data sets. For example, a customer churn model built with deep learning techniques might provide fantastic prediction accuracy but at the expense of interpretability and understanding how the model derived the answer. The business may have originally wanted a high accuracy model as well as an understanding into why customers churn.