Operations staff get a hard time. The lowly systems administrator (sysadmin), database administrator (DBA) and all the other operations engineering team members from cyber penetration specialists to user acceptance testing (UAT) and so on are generally unloved.
This reality is why we used to talk about software application developers (who, you may have noticed, have been getting a lot of the limelight in recent years) finishing their application builds and then just ‘throwing it over the wall’ to the operations team to figure out how to provision backend systems and be able to run it.
Things have gotten better and the DevOps developer (Dev) + operations (Ops) drive to form a more coalseced workforce ethic and culture has progressed things, but not always.
Clearly, we need to keep evolving DevOps and at the same time (and this could be the good news part for operations staff) we also need to start creating directly applied Ops functions to specific parts of the modern IT stack.
Key among these functions will be Artificial Intelligence (AI).
Enterprise data platform company Tibco wants to make life easier for the AI operations team (now commonly written as AIOps) with its ModelOps release. This software service is designed to enable businesses to deploy AI models faster across a broader range of devices, machines and user endpoints at scale. Sometimes written as TIBCO to denote the organization’s lengthy acronym (The Information Bus COmpany), the firm is known for its data integration pedigree and its data analytics portfolio.
To put it in really direct terms, this is scalable secure cloud-based data analytic model management, monitoring and governance - now with an increased focus and function set aligned to AI model deployment.
When we talk about an AI ‘model’ in this sense, the term is used to encapsulate the algorithmic logic that goes into the AI engine (or brain) and it also straddles the critical life-support systems that the AI will need in order to work without bias, without loss of insight into what it is doing and with transparency of an algorithm’s behaviour within business-critical applications
In this case, Tibco ModelOps addresses the requirement for speed in deploying AI and draws from the company’s work in data science, data visualisation and business intelligence (BI). The software itself works to get AI models to a state where they can be deployed and managed into ‘model pipelines’ (a digital journey that describes the lifeblood, location, lifecycle and lifespan of an AI model) so that they can be moved into production environments efficiently in robust ways.
