Reduce AI/ML costs and risks with MLOps, Red Hat, and Intel

For today’s financial services institutions (FSIs), the ability to reduce costs and risks while enhancing the speed of service delivery is critical to staying competitive. In this environment, artificial intelligence and machine learning (AI/ML) bring about transformation by embedding deep insights into applications. In fact, 70% of FSIs report they are already using AI/ML for use cases such as:

  • Improving customer service and lowering costs with virtual assistants, intelligent bots, or both.
  • Proactively detecting the likelihood of fraud and remediating issues in real time. 
  • Supporting underwriting and risk management with deeper, data-driven insights.
  • Using predictive analytics to advise customers on spending and saving habits.

Moving AI/ML experiments into production as intelligent applications that are implemented wherever the insight is needed is a collaborative process involving data engineers, data scientists, application developers, and IT operations staff. This process, known as MLOps, is powered by tools and technologies to support each stage of the development of AI/ML models through to deployment.2 

To rapidly create and iterate on applications using MLOps, data scientists and developers need self-service access to flexible pools of high-performance resources, reusable code, and the ability to automate the integration and monitoring of AI/ML models in applications across all environments. These users tap into public cloud to expand their capabilities, but all too often find themselves restricted to the expertise of one vendor, or find unexpected high monthly charges when system processing is not optimal for the task at hand. 

Fostering rapid innovation toward a competitive advantage, infrastructure and operations teams can better support data scientists and developers with resources that are optimized to gain full value from their efforts and business value contribution. 

Red Hat® OpenShift® combined with Intel technology allows IT teams to provide these resources in a cloud-native, hybrid cloud model for self-service access to the tools they need, with the scalability their projects demand. Teams become more productive by sharing visibility into intelligent application pipelines and AI/ML software that continuously transition from development to deployment environments, improving time to market and enhancing the business value driven by MLOps efforts. 

Red Hat and Intel deliver competitive advantage for MLOps

Red Hat and Intel can help FSIs that are adopting or expanding MLOps efforts to help create a cost-effective and reliable hybrid cloud environment so data scientists and developers can focus on delivering value to the business.

By working together to optimize performance, efficiency, and processor innovation, Red Hat and Intel help FSIs rapidly spin up, spin down, and horizontally scale intelligent services as business needs demand. Architecture-optimized AI libraries and tools support development and delivery pipelines with processors that feature built-in inferencing acceleration through Intel Deep Learning Boost (Intel DL Boost) with Vector Neural Network Instructions (VNNI).

Red Hat and Intel co-developed deployable architecture solutions that provide acceleration of enterprise digital transformation—including Intel Select Solutions for Red Hat OpenShift Container Platform—for more than 20 years. Red Hat and Intel also partner to build tested and validated private analytics clouds for FSIs, providing the flexibility and confidence to deploy cloud-native AI/ML applications. 

Discover more about the Red Hat and Intel partnership.

  1. TechTarget Custom Media. “It’s a New Era in Advanced Analytics and AI.” Commissioned by Cloudera, Inc. and IBM, Inc., 2020.

  2. Columbus, Louis. “How AI can improve financial analytics.” Forbes, July 2020.

Icon-Red_Hat-Media_and_documents-Quotemark_Open-B-Red-RGB “Deploying and scaling AI/ML can be long and cumbersome with many obstacles along the way. Many projects don’t make it into production because of model inefficiencies that slow down or halt the entire process.” [1]