Delivering the capabilities that Data Science and IT Ops teams need to work together to deploy, monitor, and manage machine learning models in production.
The percentage of AI models created but never put into production in large enterprises has been estimated to be as much as 90% or more. With massive investments in data science teams, platforms, and infrastructure, the number of 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 that need to be managed.
Barriers to AI Success
Difficulty with deployment. Data science teams are using a variety of ML platforms, languages and frameworks to build models that may not conform to the requirements for production applications.
Managing change. With predictive models, prediction accuracy can decrease without changes in traditional software indicators like memory usage or response time, making it impossible to use traditional application performance monitoring tools.
Managing complexity. AI-based applications have a complex lifecycle, including frequent updates to models and the introduction of new, competing models to improve performance, which significantly complicates their management.
Managing risk and regulations. AI 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 from any ML platform on modern production infrastructures such as Kubernetes and Spark on any cloud or on-premise.
- Monitor ML-based applications for performance issues with ML-centric capabilities like data drift analysis, model-specific metrics and infrastructure monitoring and alerts.
- Manage the dynamic nature of machine learning applications with the ability to frequently update models, test new, competitive models, and change applications on-the-fly while continuing to serve business applications.
- Enforce governance policies related to ML deployment and capture the data that is required for strong governance practices, including who is publishing models, why changes are being made, and what models were in place over time.
Realize Tangible Value and ROI from AI with DataRobot MLOps and Governance:
- Model deployment in hours not months
- Continuous ML health monitoring
- On-the-fly model management
- Built-in ML governance best practices