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KPMG Automates AI Workflows with Red Hat OpenShift


Red Hat and KPMG LLP have announced an ongoing collaboration to augment the KPMG Ignite AI platform with Red Hat OpenShift as a foundational technology. Building on Red Hat OpenShift, KPMG Ignite provides the agility, scalability and flexibility needed to deploy AI at scale, and enables Ignite to be deployed more consistently across the hybrid cloud.

According to the KPMG recent AI study, “Thriving in an AI World”, the rate of AI adoption skyrocketed in many industries because of COVID-19, but many leaders feel this uptick is moving too quickly. The study indicates however, that organizations who prioritize AI in their operations can better know and serve their customers, automate repetitive operations, better inform business strategy and drive greater innovation. To capitalize on these benefits, many of KPMG clients as well as KPMG itself seek to embed AI through multiple IT functions into their overall organizational technology fabric providing better management and analysis of their AI data.

To help meet this need, KPMG offers the Ignite AI platform. Ignite is a U.S.- patented portfolio of AI capabilities that brings together machine learning, document ingestion and optical character recognition capabilities to help analyze and decipher both structured and unstructured data. Ignite focuses on automating, accelerating and enhancing existing AI solutions so organizations can achieve real value from data to make better business decisions across an entire organization.

KPMG chose Red Hat OpenShift as an enabler of AI across a broad set of modern footprints, providing more flexibility for clients to work across the hybrid cloud, from private clouds to multiple public cloud environments. As the underlying Kubernetes platform, Red Hat OpenShift is a key element for Ignite, based on its ability to provide greater agility, flexibility, portability and scalability for nearly any AI workload in almost every enterprise IT deployment. OpenShift also provides security features and application controls, along with robust, native continuous integration and continuous deployment (CI/CD) capabilities, helping to more quickly operationalize AI capabilities into production with greater security.

This flexibility is necessary to more rapidly develop, deploy and run machine learning (ML) models and associated intelligent applications in production while mitigating risk of being locked into a single cloud provider or hardware stack. Additionally, with the foundation of Red Hat OpenShift, data scientists using the platform can focus on ML modeling and deployment without having to act as IT operations teams or systems administrators.

KPMG has also formed a strategic alliance with Red Hat to provide and enhance these hybrid multi-cloud experiences for clients, bringing greater choice, control and freedom of open source to fuel digital acceleration. This innovative technology approach affords organizations the flexibility of working with and across several of its cloud alliance partners.

Joe Fernandes, vice president and general manager, Cloud Platforms, Red Hat, says, “AI solutions are changing the way we do business, enabling organizations to better serve their customers and get more done quicker – but they must be built on a hybrid cloud platform that can help deliver stable, production-ready innovation. With Red Hat OpenShift, KPMG Ignite has a hybrid cloud platform with the flexibility and scalability required to accelerate AI/ML initiatives from pilot to production, helping advance their clients’ digital transformation initiatives.”

Kevin Martelli, principal, software engineering, KPMG LLP, says ““When determining the underlying technology platforms for Ignite, we needed technology that was flexible and easy-to-use that offers enterprise-grade security across a hybrid cloud. With Red Hat OpenShift, containers and Kubernetes are at the center of Ignite, providing data scientists and developers the much-needed agility, flexibility, consistency, portability, and scalability to train, test, and deploy machine learning models anywhere.”

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