MLOps Foundations Service Block

Operationalize machine learning across your entire business

Companies are realizing the value of using machine learning models to drive better outcomes for their businesses. Harnessing the predictive power of their data with machine learning models to remain competitive is becoming more critical to business operations, yet 60% of machine learning models never make it to production.

With MLOps Foundations, Rackspace Technology accelerates operationalizing models to achieve the benefits of an automated machine learning lifecycle management, by reducing the typical 25+ step lifecycle down 10 steps for a significantly faster delivery time.

Machine Learning is complicated so feel free to speak an ML expert by signing up or read the detailed description of our service block.


Key Benefits

The Model Factory Framework is built using AWS services and open source tools that enable rapid development, training, scoring and deployment of models. The MLOps Foundations Solution can be tailored to specific workflows and business needs through customization around the model factory framework.

  • Tools for diagnostics, performance monitoring and addressing model drift]
  • Model explainability for governance and regulatory compliance
  • Platform for collaboration
  • Accelerated ROI
  • Standardized model development environment for your data science teams
  • Automated model deployment across dev, Q/A and production environments
  • Reproducibility of models and predictions

Key Features

  • Discovery Session
  • Design Decisions
  • Implementation of the Model Factory Framework
  • Integrate machine learning models

Rackspace Model Factory Framework FeaturesMachine Learning Model Integration and Pipeline Development
CI/CD integration with automation and orchestration toolsIntegration of reproducible machine learning piplelines
Experiment trackingIntegration of reusable software environments for training and deploying models
Project packaging for easy source control and reproducibilityAutomated triggering
Register, package, train and deploy models from anywhereEnd-to-end QA test and performance check
Support for major ML frameworks such as TensorFlow, SCikit-Learn, Spark machine learning, spaCy, PyTorch, etc.Guidance to team collaboration and best practice
Real-time inference endpoints
Scalable scoring
Model artifact storage
Tracking user interface


You may withdraw your consent to receive additional information from Rackspace Technology at any time. Information collected in this form is subject to the Rackspace Technology Privacy Notice.