Top Projected Trends in Machine Learning for 2023

By Swapnil Srivastava, Saikat Choudhury, Arjun Vishwanath

Let’s Talk ML

It’s been 67 years since Stanford professor John McCarthy defined artificial intelligence as “the science and engineering of making intelligent machines.”

Machine learning (ML) helps businesses find insights hidden in huge quantities of data while avoiding expensive and time-consuming traditional methods of analysis.

This guide will walk you through the top trends in ML for 2023 and provide some examples of how it can help your business.

Refresher: What is Machine Learning?​

Machine learning (ML) algorithms use data to learn, adapt, recommend, and draw inferences. Unlike rules-based programs, ML algorithms become more accurate as they iterate.

Machine learning has already become one of the most common forms of AI in the business world. The creation of incredible amounts of data and the inception of deep learning and artificial neural networks fueled the rise of ML. 

Companies often deploy ML to reveal insights hidden in huge quantities of data that no humans could study at that scale or pace.

Trends in Machine Learning for 2023

Trend 1: Rise of MLOps​

Machine Learning Operations (MLOps) is a practice that improves ML model accuracy and efficiency. The complex and compounding challenges generated by multiple stakeholders implementing ML create the need for the practice.

By 2025 the MLOps market is expected to expand to almost

$ 0 billion.

Trend 2: Data Governance​

Organizations realize a solid data and ML governance strategy is critical to delivering value through their ML efforts while ensuring standardization and ownership.

TechBeacon says, “Operating ML models without good governance in place allows flawed processes to produce unwanted results – often quickly and repeatedly.”

Trend 3: Use of Third-Party Data​

Companies realize that only using internal data leaves gaps in their understanding of the market. To address this need, businesses are increasingly making use of publicly available reports, news, and records. 

The MIT Sloan Management Review found that analytically mature businesses were more likely to leverage external data sources, such as customers, vendors, regulators, and competitors.

Third-Party Data Use Case: Pricing Simulator​

Challenge

A B2B industrial manufacturer found pandemic market shifts made its pricing process more complex and challenging. It wanted to optimize its pricing and improve margins to remain competitive.