It is becoming more and more important for businesses to use Machine Learning (ML) models to take advantage of data’s predictive power. However, 60 percent of ML models never make it to production. These models often fail to deliver the intended results because of inadequate considerations of the shift in people, processes, and platforms needed to operate in a responsive, agile technology environment effectively. The new Rackspace ML Operations Foundations offering (MLOps), powered by the Rackspace Technology Model Factory Framework, can help you innovate your data landscape, even if you don’t have all the technical expertise to move ML models into production efficiently.
Rackspace Technology built our Model Factory Framework with open source tools that enable rapid development, training, scoring, and deployment of models. We can tailor the MLOps solution to specific workflows and business needs through customization around the Model Factory Framework. Rackspace leverages the innovative capabilities of the cloud to help customers build new revenue streams, increase efficiency, and deliver incredible experiences. Rackspace teams employ industry best practices and a wide range of expertise across data migration, data engineering, data science, ML, and operations to help turn your data into a tool for innovation.
Features and benefits
Standardized model development environment for your data science teams: Allows your data science and engineering teams to develop models with support for any ML framework.
Automated model deployment across development, quality assurance (QA), and production environments: Leverages hyper-scale cloud automation to quickly take models from development to deployment, while addressing the production-ready requirements of high availability, scalability, reliability, and cost management.
Reproducibility of models and predictions: Tracks model versions and hyper-parameters, when deploying to different environments, ensuring that model performance in development matches model performance in production.
Tools for diagnostics, performance monitoring, and addressing model drift: Provides tools for data science teams to address model drift, model performance for debugging and troubleshooting, and pipeline management.
Model explainability for governance and regulatory compliance: Track models, data, and parameters to show auditors that you employed the proper protocols.
Platform for collaboration: Combines DevOps tools and processes with ML workflows. Teams can work closely together to develop complex models and pipelines and manage model progression through the different development stages to production, with respective versions, annotations, and history.
Accelerated ROI: Expedite the use of models in production and realize the benefits of an automated cloud-based ML lifecycle management solution with a pay-for-what-you-use model. This feature includes tools and services to help you monitor and optimize your compute resources.
Conclusion:
With MLOps, Rackspace can help you reduce the typical 25+ step lifecycle to 10 steps or less. This efficiency allows you to put models into operation in production much more quickly.
The Model Factory Framework enables support for numerous ML models in production and development through Continuous Integration/ Continuous Development (CI/CD) for data science and automated workflows for development, QA, and production. Rackspace experts tailor this service to your specific workflows and business needs while implementing relevant best practices.
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