Red Hat
flow-image

Top considerations for building a production-ready AI/ML environment

Published by Red Hat

This guide outlines key considerations for creating a production-ready AI/ML environment, emphasizing technologies and strategies to support scalable and reliable AI/ML workflows. Key insights include:

  1. Data Utilization: Leveraging data effectively is crucial to drive insights and optimize operations. AI/ML models can enhance decision-making, streamline processes, and deliver customer-centric solutions across industries, from healthcare to telecommunications.
     
  2. Core Technologies:
    • Containers and Container Orchestration: Enable deployment of AI/ML tools across environments, facilitating flexibility and security.
    • Application Lifecycle Management: Supports scalability and automated management for continuous development.
    • MLOps Practices: Ensures efficient collaboration across teams, fostering CI/CD practices for quicker model deployment.
    • Hybrid Cloud and Edge Deployments: Provide a flexible infrastructure that supports model training and inferencing across environments.
       
  3. Open, Flexible Foundations: Red Hat OpenShift provides a scalable, unified platform for building AI/ML applications, supporting integration with various partners like NVIDIA, Intel, and SAS, for seamless model deployment and management.
     
  4. Implementation Challenges: Organizations may face talent shortages, data preparation hurdles, and infrastructure inefficiencies. The guide recommends using cloud-native approaches and automation to mitigate these issues and accelerate deployment.
     
  5. Case Studies: Examples highlight successful applications of Red Hat AI/ML solutions, including customer onboarding optimization, edge data analysis, and academic platforms for AI/ML education.

In summary, a robust, containerized, and hybrid cloud-based architecture, combined with MLOps best practices, is critical for creating a scalable, production-ready AI/ML environment.

Download Now

box-icon-download

Required fields*

Please agree to the conditions

By requesting this resource you agree to our terms of use. All data is protected by our Privacy Notice. If you have any further questions please email dataprotection@headleymedia.com.

Related Categories Machine Learning, Artificial Intelligence, Deep Learning, Cognitive Computing, NLP, AI in Business, AI-powered Analytics, AI in Healthcare, Business Analytics, Business Process Automation (BPA), Environmental, Social Governance Analytics (ESG), Supply Chain Resilience

More resources from Red Hat