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.
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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 Features | Machine Learning Model Integration and Pipeline Development |
|---|---|
| CI/CD integration with automation and orchestration tools | Integration of reproducible machine learning piplelines |
| Experiment tracking | Integration of reusable software environments for training and deploying models |
| Project packaging for easy source control and reproducibility | Automated triggering |
| Register, package, train and deploy models from anywhere | End-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 |
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