TIBCO goes DIY with new AI-model deployment platform

TIBCO goes DIY with new AI-model deployment platform

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Enterprise data management provider TIBCO has joined a trending group of software developers that are making the usage of AL and ML easier to understand and deploy for non-data science-trained, line-of-business employees at companies utilizing this on a regular basis. 

The Palo Alto, California-based company today announced the release of TIBCO ModelOps, which enables businesses to deploy AI models faster and at scale to keep up with the pace of their sales activity. This addition to TIBCO’s analytics portfolio helps users to build their models and scale cloud-based analytic model management, deployment, monitoring and governance.

ModelOps is format-agnostic, supporting all common model formats, including API-based models in any cloud service or on-premises, Mark Palmer, TIBCO SVP of engineering, told VentureBeat. 

“The way we approach this is that we use low-code/no-code tooling, cloud-based architecture; we accept any AI model, which is pretty important because some of the platforms prefer their own authoring environments,” Palmer said.

Building AI models that are effective can be time-consuming and tedious. ModelOps addresses the need for speed in deploying AI and draws from 25-year-old TIBCO’s experience in data science, data visualization and business intelligence. This aids AI teams in confronting common deployment hurdles, such as applying analytics to applications, identification and mitigation of bias, and transparency and manageability of an algorithm’s behavior within business-critical applications. 

The open-standards platform enables businesses to deploy and manage model pipelines directly into production environments, Palmer said. 

“While 92% of firms spent more overall on data science in 2021 compared to previous years, only 12.1% deployed it at scale,” Palmer said. “We’ve designed a system that puts self-service access to data science firmly in the hands of teams, including business users. This allows decision-making teams to choose the algorithm they want, work from any cloud service, and run it safely, securely, and at scale. This is a bold step to enabling business users to take AI out of the lab and out on the road.”

ModelOps fits right in for the company’s users to add governed models to other TIBCO tools, including Spotfire, Data Virtualization and Streaming, Palmer said. 

“The ability to quickly deploy, measure, and adjust models of any kind – machine learning models, python code, rules and more – is essential to our success,” InSoo Ryu, technical leader, SK Hynix said in a media advisory. “This is the right platform to scale our data science efforts with a more governed, process-oriented approach to data science operationalization.”

A recent survey of its customers confirmed to TIBCO that it’s no longer uncommon for organizations to manage hundreds – even thousands – of analytic models and workflows, Palmer said. ModelOps allows any authorized business user, data scientist, analyst or IT user to manage and deploy thousands of models in production with complete governance and management capabilities. Users are able to deploy in the cloud or on-premises, he said.

TIBCO competes in a market that includes Microsoft, IBM, Informatica, Oracle, Denodo, SAP, Amazon Web Services (AWS), Talend, and several more, according to Gartner Research.

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