Machine Learning Operations (MLOps)
The share of AI models created but never put into production at large enterprises has been estimated to be as high as 90% or greater. With massive investments in data science teams, platforms, and infrastructure, the number of undeployed AI projects is dramatically increasing — along with the number of missed opportunities. Unfortunately, most projects are not showing the value that business leaders expect and are introducing new risks.
Barriers to AI Success
Difficulty with deployment: Data science teams are using a variety of ML platforms, languages, and frameworks that rarely produce production-ready models. Wary IT teams are unwilling or unable to deploy code they don’t understand.
Flying blind: With predictive models, prediction accuracy can decrease without changes in traditional indicators like memory usage or response time, requiring new monitoring methods and metrics.
Complex updates: AI-based applications have a complex lifecycle, including frequent updates that, when done manually, are time-consuming. Model updates also require significant production testing and validation to maintain production model quality.
New risks and regulations: IT operations machine learning applications need strong governance practices and tools to minimize risk and ensure regulatory compliance, which many organizations have not put into place.
- Easily deploy machine learning projects written in modern languages and frameworks, on modern production infrastructures such as Kubernetes on any cloud or on-premise system.
- Monitor ML-based applications for performance issues with ML-centric capabilities like data drift analysis, model-specific metrics, and alerts. Provides proactive management and timely updates that don’t waste resources and ensure continued application performance.
- Manage the dynamic nature of machine learning applications with the ability to frequently update models, including testing and validation of new models. Update models on-the-fly while continuing to serve business applications.
- Enforce governance policies related to machine learning models and capture the data that is required for strong governance practices in machine learning operations management, including who is publishing models, why changes are being made, and which models have been deployed over time.
Realize Tangible Value and ROI from AI with DataRobot MLOps and Governance:
- Model deployment in hours not months
- Proactive ML health monitoring
- Efficient and trusted model updates
- Built-in ML governance best practices