IBM Consulting Global AI at Scale on Azure

IBM Services for AI at Scale on Azure focuses on these three key pillars:

1. Reference architecture for Azure data platform and complete end-to-end MLOps pipeline combining Azure native services with open source MLApp framework developed by IBM Services

2. Model governance patterns leveraging leading open source utilities built by IBM Research for Robustness, Fairness, Explainability and Transparency deployed on Azure

3. An engagement and operating model based on IBM Garage Methodology to scale AI and reduce time to value

IBM Services for AI at Scale is a “consult-to-operate” service that will consistently integrate and scale AI/ML. It brings the engineering discipline to architect, engineer, deploy and monitor the AI/ML models while also satisfying the strategic imperatives of AI to be Trustworthy, Scalable and Sustainable.

IBM Services for AI at Scale uses our Rapid Asset Development for Machine Learning (RAD-ML) framework which defines standard solution components and provides high value accelerators such as Code Frameworks, Reference Architecture, and Best Practices. The code framework has been developed and released as Open Source Python library called MLApp. It includes Embedded MLOps, Project Scaffolding, Model Boilerplates, Data Connectors and Utilities for model tuning and deployment.

Benefits of IBM Services for AI at Scale

– Reduce model deployment costs. Building a portfolio of reusable assets exponentially increases data science and developer productivity.

– Accelerate deployment. Models make it to production more quickly, generating promised business value.

– Increase growth. Scaling analytics, joint experimentation and co-creation with data scientists opens up new possibilities.

– Reduce production support costs. Outsourcing AI and ML model management and support improves efficiencies.

Business Outcomes:

4x faster time to value

20-40% increase in growth

25% reduction in POC costs in production