Just a few years ago, the idea of integrating AI/ML into a business workflow was the stuff of fanciful startups and Silicon Valley dreams. But as the practice has begun to solidify as a legitimate business tool for large enterprises, AI/ML has become more than just a great way to spend compute resources. It's a path to surfacing better customer experiences, creating better outcomes and even to saving lives.
All of these benefits don't just arrive on the scene because an administrator installed some software, however. AI/ML at scale, and the the speed of business, is one of those technology spaces that has now merited its own term: MLOps.
While the fundamentals of MLOps are squarely rooted in DevOps, the actual implementation of these algorithms and the usage of them day to day falls onto the same teams that are tasked with implementing the digital transformation: the developers.
Enabling those developers requires robust systems of data management, ingestion, and processing, similar to classic business ETL work, but at significantly larger and faster scales. Additionally, the specific use case for each AI/ML application is unique and must be tailored to meet the individual needs. While the systems enabling MLOps can be reused for multiple projects, the actual AI/ML projects themselves can range from matching buyers to goods, to predicting medical conditions ahead of time.
Today, unlocking the business power of AI/ML is no longer an item on the future roadmap. It is a differentiator that can make the difference here and now. Just ask any of our customers> here's a whole swath of case studies and information about how businesses are using Red Hat products to operationalize AI/ML.
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