Verta launches Enterprise Model Management system for AI-driven enterprises, amidst continued momentum - The EE

Verta launches Enterprise Model Management system for AI-driven enterprises, amidst continued momentum

Palo Alto, United States – Verta, the Operational AI company, announced momentum with continued adoption and enhancement of its Operational AI platform, the launch of its Enterprise Model Management system for AI-driven enterprises, the debut of its Verta Insights research group to conduct primary research on artificial intelligence and machine learning, and expanded hiring of industry experts to meet growing market demand and delivery to support its customers.

The Verta Operational AI platform enables stakeholders across MLOps – including data science, ML engineering, DevOps, risk and governance to package and deliver any model instantaneously using DevOps support for CI/CD, security and monitoring, while ensuring reliable and scalable real-time AI deployments.

The Verta Enterprise Model Management system centrally organises all model assets across the enterprise, with integrated experiment tracking, deployment management, versioning and monitoring to establish governance, enable collaboration, and deliver visibility into model performance and usage. It has capabilities to manage end-to-end metadata states and software lifecycles across development, production and archival processes. The Verta system is model-agnostic and hybrid across multi-cloud and on-premise sources.

“With Enterprise Model Management, companies are able to accelerate machine learning pipelines and deliver faster time to value from ML projects, with enhanced discoverability and reuse, clear visibility into performance and usage, and model risk management and governance to enable Responsible AI and regulatory compliance,” says Manasi Vartak, founder and CEO of Verta. “Our customers use the Verta Operational AI platform and Verta Enterprise Model Management to enable the real-time machine learning required for the next generation of AI-enabled intelligent products and services.”

Recent updates to Verta’s Operational AI platform and Enterprise Model Management solution enable greater cross-enterprise visibility and collaboration around model assets, next-generation model tracking and reporting capabilities, and enhanced security and model risk management capabilities.

Verta also announced its Verta AI Leaders (VAIL) Accelerator program for Insurance, designed to help insurers increase model velocity and the subsequent realisation of value. With the Accelerator program, insurers get access to Enterprise Model Management to centrally organise, manage, and describe enterprise AI/ML model assets and enable collaboration on real-time AI and ML use cases to maintain competitive advantage. Enrollment in the VAIL program is open through December 1, 2022.

Launch of Verta Insights

Verta launched its Verta Insights research group to conduct research into trends in the AI and machine learning space. Verta Insights delivers practical insights to assist AI/ML practitioners and executive leaders for adopting Operational AI technologies and best practices. The first Verta Insights research study, State of Machine Learning Operations 2022, showed that fewer than half of organisations have put in place the tools they will need to manage the rapid expansion they expect in real-time uses of machine learning (ML).

According to the study, which included feedback from more than 200 machine learning practitioners, more than two-thirds (69%) of participants said that real-time use cases would be increasing or increasing significantly over the next three years. At the same time, fewer than half (45%) of all participants reported that their company has a data or AI/ML platform team in place to support getting models into production, and just 46% reported having an MLOps platform in place to facilitate collaboration across stakeholders in the ML lifecycle, suggesting that the majority of companies are unprepared to handle the anticipated increase in real-time use cases.

“The tooling and IT infrastructure, as well as the skillsets, required to support real-time machine learning are different from those needed to support batch, analytical workloads,” notes Rory King, head of marketing and research of Verta. “The Verta Insights State of Machine Learning Operational study confirmed that while companies are increasing their use of real-time use cases, they have not made the investments necessary to support real-time ML.”

King says that the rapid increase in real-time machine learning use cases is driving organisations to augment their technology stack to include Operational AI infrastructure, enabling them to realise the top line benefits they expect from intelligent equipment, systems, products and services. “We also see companies setting up machine learning platform teams to manage this essential technology infrastructure to deliver the same levels of availability and uptime that they expect from other business critical enterprise systems,” King adds.

Growing market demand

Vartak invented modern-day experiment management and tracking when she created ModelDB, the progenitor of MLflow, and Verta’s founders accrued extensive hands-on experience in data science and operational ML at AI-forward tech giants like Google, Twitter and NVIDIA. They established Verta to fill a gap they saw in the tooling to operationalise ML that was preventing companies from realising the full value of data science and machine learning.

Verta enables organisations to ship AI-enabled products and detect model quality issues 10-30x faster than previously. Customer successes include:

  • A Fortune 100 insurance company used Verta to consolidate multiple homegrown systems for model deployment, which allowed them to streamline their deployment, increased ML productivity, and gave them full visibility and control over their machine learning assets.
  • A top-20 US auto insurance company is using Verta to enable real-time machine learning, which allowed the company to launch its real-time AI applications.
  • A digital content library used Verta to make model registration and deploy fully self-service, which accelerated their model delivery and allowed them to ship AI-enabled products 10 times faster.

Vartak notes that Verta has been expanding its team to meet increased market demand for Operational AI solutions, including adding to the company’s development, customer success and go to market teams. “We’re pleased that the market has recognised the need for tools like Verta to support Operational AI and Enterprise Model Management. We’re making investments in our team to enhance our platform and deliver the promise of Operational AI to a growing list of customers in industries like insurance, banking and financial services,” Vartak says.

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