Machine Learning (ML/AI) Infrastructure Strategy

Crafting a comprehensive ML ecosystem includes lifecycle management, tooling considerations, and much more.

Finding sustainable business value in ML/AI ecosystems.

The availability of inexpensive, easy-to-use machine learning (ML) engines has resulted in incredible advances in the application of this life-changing technology — everything from consumer advertising to industrial process control for beer brewing. So many innovative ideas for applying ML (and AI), so little time!

But to use ML effectively in production, the ML engine itself is only one piece of a very large puzzle. A complete ML ecosystem includes many other core components, including (but not limited to):

  • Configuration
  • Data collection
  • Data verification
  • Feature extraction
  • Machine resource management
  • Process management tools
  • Analysis tools
  • Cybersecurity protection
  • Integrity protection
  • Server infrastructure (and auto-scaling)
  • API management
  • Monitoring
  • Data retention

Full-stack lifecycle management + ML tooling expertise.

Deploying and operating a full ML ecosystem involves architecting a modular environment where lifecycle management of subsystem components can increase supportability and reliability. This approach results in a production-grade ML ecosystem that supports your organizational needs.

Rule4 pairs the best in full-stack lifecycle management with ML tooling expertise to help your organization architect and deploy a complete ML ecosystem that provides sustainable business value now and into the future.

A lasting implementation needs a solid foundation.

Let’s build a production-grade ML ecosystem for your organization.