Applied AI in 2022: Chips, MLOps, and Enterprise Maturity
AI adoption surges as it becomes a practical business tool rather than a research curiosity. Key trends shaping applied AI in 2022 include a booming market for specialized AI chips and a growing focus on operational machine learning workflows.
AI adoption skyrockets as more professionals recognize its practical value in the enterprise. Surveys indicate that company culture is no longer the primary barrier to implementation, which means artificial intelligence matures rapidly and takes center stage for major tech giants like Microsoft and Amazon.
A new generation of specialized AI chips transforms how organizations run machine learning workloads. Cloud providers like Google and Amazon develop their own proprietary hardware, while Nvidia continues to dominate the market and emerging startups achieve unicorn status. This hardware expansion gives practitioners diverse options beyond traditional CPUs and GPUs, allowing AI to run more economically and accelerating time to market.
Alongside hardware advancements, operational machine learning workflows and data-centric approaches become essential pillars for applied AI. Selecting the right infrastructure now involves navigating many complex parameters that impact both engineers and end users. As these technologies converge, organizations dedicate more resources to deploying effective AI solutions rather than just building foundational models.