CodeFlare can integrate emerging workflows with complex pipelines that require the integration and coordination of different tools and runtime environments. It is also designed to scale complex pipelines such as multi-stage NLP, complex time series and forecasting, reinforcement learning, and AI workshops. The framework can integrate, run, and scale heterogeneous pipelines that use data from multiple sources and require different processing.
The research blog also says CodeFlare can save scientists money months of work on large pipelines, providing the data team more time for production and development work.
CodeFlare is aA purpose-built platform that provides comprehensive end-to-end pipeline visibility and analytics for a wide variety of machine learning models and workflows. It provides a simpler way to integrate and scale entire pipelines while providing a unified runtime and programming interface.
For these reasons, despite historically high failure rates of AI models, Moor Insights & Strategy estimates that machine learning models using Co deFlare pipelines will have a high percentage of machine learning models moving from experimental to production status.
- IBM plans to improve CodeFlare to support increasingly complex pipelines.
- Future development plans should include improved fault tolerance and support for pipeline visualization.
- IBM made CodeFlare available on the GitHub project Repository "CodeFlare . There are also examples which "s 'run on IBM Cloud and Red Hat OpenShift.
Note: The writers and editors of Moor Insights & Strategy may have contributed to this article.
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