How should IT leaders proceed with all these Ops promising smoother and more responsive service delivery? As with any promising technology overhaul, a rethinking of processes and culture is essential.
DevOps — which fosters greater collaboration and automation in software delivery — is only the beginning of a new phase of technology management. Now, we are seeing many spinoffs — DataOps, Machine Learning Operations (MLOps), ModelOps — and other Ops that seek to add speed, reliability, and collaboration to the delivery of software and data across enterprise channels. There is even a DataOps Manifesto, which bears a striking resemblance to the Agile Manifesto crafted back in 2001. However, none of this stuff is going to happen overnight. Or even within a few months. As with any promising technology overhaul, a rethinking of processes and culture is essential. Where does that leave IT managers and professionals? How should they proceed with all these Ops promising smoother and more responsive service delivery? „A key element of preparation is to ask the important questions about existing processes, both formal and informal,“ says Alice McClure, director of artificial intelligence and analytics for SAS. „This helps identify where to focus first, what needs to be updated and where bottlenecks exist.“ DataOps, for one, „provides an agile approach to data access, quality, preparation, and governance — the entire data lifecycle, from preparation to reporting,“ says McClure. „It enables greater reliability, speed and collaboration in your efforts to operationalize data and analytic workflows. ModelOps is becoming a must-have methodology for implementing scalable predictive analytics. It’s all about getting analytics into production – iteratively moving models through the analytics life cycle quickly while ensuring quality and enabling ongoing monitoring and governance of models over time.“ It’s all about bringing together automation and architecture, advises Amar Arsikere, CTO and co-founder at InfoWorks.
