Closing the gap between their organization's choice to invest in a data science and machine learning (DSML) strategy and the needs that business units have for results, will dominate data and analytics leaders' priorities in 2022. Despite the growing enthusiasm for DSML's core technologies, getting results from its strategies is elusive for enterprises. Market forecasts reflect enterprises' early optimism for DSML. IDC estimates worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services will grow 15.2% year over year in 2021 to $341.8 billion and accelerate further in 2022 with 18.8% growth, reaching $500 billion by 2024. In addition, 56% of global enterprise executives said their adoption of DSML and AI is growing, up from 50% in 2020, according to McKinsey.
Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.
In this week's real-time analytics news: HPE launched HPE Swarm Learning, a privacy-preserving, decentralized machine learning framework for the edge. Keeping pace with news and developments in the real-time analytics market can be a daunting task. We want to help by providing a summary of some of the important news items our staff came across this week. Hewlett Packard Enterprise (HPE) announced the launch of HPE Swarm Learning, an AI solution to accelerate insights at the edge, from diagnosing diseases to detecting credit card fraud, by sharing and unifying AI model learnings without compromising data privacy. HPE Swarm Learning is a privacy-preserving, decentralized machine learning framework for the edge or distributed sites.
As machine learning and AI propagate in software products and services, we need to establish best practices and tools to test, deploy, manage, and monitor ML models in real-world production. In short, with MLOps we strive to avoid "technical debt" in machine learning applications. SIG MLOps defines "an optimal MLOps experience [as] one where Machine Learning assets are treated consistently with all other software assets within a CI/CD environment. Machine Learning models can be deployed alongside the services that wrap them and the services that consume them as part of a unified release process." By codifying these practices, we hope to accelerate the adoption of ML/AI in software systems and fast delivery of intelligent software.
McKinsey reports AI adopters with a proactive strategy achieve significantly higher profit margins -- between 3% and 15% above the industry average. Today, two-thirds of executives cite AI as vital to the future of their business, with plans to increase investments this year. As a result, IDC reports the global AI market is forecast to accelerate further in 2022 with 18.8% growth and remain on track to break the $500 billion mark by 2024. MLOps--machine learning operations, or DevOps for machine learning--enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. MLOps, or machine learning operations, refers to the process and tooling of consistently developing, deploying and maintaining reliable, responsible AI.