IT, infrastructure, and data engineering teams are increasingly focusing on AI/ML initiatives as a means to drive top-line revenue and control bottom-line costs. In order to stay as competitive as possible, companies are backing this agenda with practical investments.
This accelerated pace of investment brings a new set of challenges. IT needs a way to meet the high bar around governance and compliance, while technology leaders need effective tools to meet these requirements. As organizations taking a manual route to production ML typically encounter issues with governance, automation might be the key to resolving these bottlenecks. Their efforts, however, might be misaligned with IT needs and capabilities and they may fail to get the desired business results.
In our ebook, MLOps for IT Teams, you’ll see some of the challenges IT, infrastructure, and data engineering teams face when trying to scale ML efforts, as well as some of the ways robust machine learning operations (MLOps) solutions can help deliver the promise of AI.
