ModelOp Announces New Release of ModelOp Center
CHICAGO, March 31, 2020—ModelOp announced new capabilities in its ModelOp Center V2. ModelOp Center V2 is the only technology and vendor-agnostic ModelOps software platform designed to deploy, monitor and govern any kind of model. This includes models produced using any analytics or data science tool, in any enterprise infrastructure – on-premises, cloud or hybrid cloud. The expanded platform, with its new Command Center and enhanced user experience, delivers significant, industry-first efficiency gains that enable enterprises to realize the value of their AI investments. A volatile economic and business landscape further underscores the imperative to manage models with agility to respond to rapidly changing circumstances.
The company also announced today a Series A funding round of $6 million, led by Valley Capital Partners with participation from Silicon Valley Data Capital.
According to IDC, only 35% of organizations indicate that analytical models are fully deployed in production. With the majority of models never used in business applications, those that do make it into production typically do so only after long delays and are rarely refreshed at the rate needed to maintain accuracy. The accumulated “Model Debt” of undeployed and unrefreshed models leaves millions of dollars on the table, since these models don’t reach their intended purpose and expected value. At the same time, AI is becoming more pervasive with the rise of Augmented Machine Learning (ML) and Citizen Data Scientists. As discrete business units implement these easier-to-use solutions, the lack of enterprise-wide visibility and control can lead to unmanaged “Shadow AI.” These rogue AI implementations increase risk, as models may run in production without accountability to IT or governance organizations.
The industry recognizes that ModelOps is the key capability that resolves these challenges for enterprise AI. According to Gartner*, “ModelOps platforms offer the following advantages for AI operations:
- Accelerated delivery of AI products to business users;
- Better alignment between business and domain experts, data science and engineering;
- Constant feedback on modeling outputs by business and/or operational experts; and
- Governance and quality assurance of models and modeling outputs in conjunction with business domain experts.”
“Recent events and market volatility have challenged many people’s assumptions about how fast the ‘ground truth’ underlying our models can change,” said Joe Squeri, CTO/COO at Exos, former CIO of Barclays and Managing Director, Technology, at Goldman Sachs. “Having the ability to update and refresh our models quickly, without friction, while ensuring compliance was a major reason for our investment in ModelOps capabilities, and in the new world we live in this will only become more important.”
The new capabilities in ModelOp Center provide a comprehensive, unifying ModelOps framework that places no restrictions on the tools used by data science teams to develop models or the infrastructure used by operational teams to deploy models in production. ModelOp Center provides full transparency and coordination for all stakeholders, including lines of business, data science, IT operations, and governance organizations.
“At the heart of founding ModelOp in 2016, was the conviction that data science and machine learning models require new organizational and technical approaches to realize their value,” said Pete Foley, CEO of ModelOp. “Our customers are at various stages in their AI journey, and our sole mission is to enable them to operationalize AI at scale. The new capabilities to ModelOp Center enable us to continue to deliver on that mission.”
ModelOp Center is the industry’s only operations-first command center for ModelOps, and supports any enterprise in any stage of their AI journey to move from the experimental phase to production. Extending this foundation, Version 2 delivers sophisticated deployment and governance capabilities that reduces Model Debt and mitigates Shadow AI through its new model life cycle (MLC) process engine and end-to-end model lineage.
At the core of ModelOp Center is its deployment, monitoring, and governance capabilities. Version 2 of the platform:
- Saves data scientists time with streamlined model registration in a comprehensive model catalogue. Plug-ins to Jupyter and other popular model creation platforms automate the initial and ongoing steps in moving models into production and keeping them refreshed;
- Provides 24/7 monitoring of critical business, statistical, and technical metrics against SLAs with automated notifications and alerting via the updated Command Center. Dashboards customized for each role (i.e. business manager, data scientist, ITOps, governance) also allow for full transparency and auditability into all models across their entire life cycle, and seamless navigation and troubleshooting as needed; and
- Supports governance through model metadata capture, enables quality and compliance control, traceability of end-to-end model lineage, and integrated multi-tenancy support.
ModelOp Center Version 2 is already in use by customers in the financial services, manufacturing and pharma industries, and is available immediately for existing and new ModelOp customers.
For more information about ModelOp’s unique approach and its solutions portfolio, please visit https://www.modelop.com/ for data sheets, videos and more.
For more information about ModelOp Center – Version 2.0, please visit https://www.modelop.com/product/.
*Gartner Inc., Assessing DevOps in Artificial Intelligence Initiatives, Carlton Sapp, Feb. 21, 2020.
ModelOp enables large enterprises to address the critical scale and governance challenges necessary to fully unlock the transformational value of enterprise AI and Machine Learning investments. The ModelOp Center platform is the essential business accountable software solution that automates the complete life cycle for models, regardless of where they are created or deployed. Fortune1000 companies in financial services, manufacturing, healthcare and other industries rely on ModelOp to put their models into business.