Create And Scale Complex Artificial Intelligence And Machine Learning Pipelines Anywhere With IBM CodeFlare Posted on : Oct 13 - 2021

To say that AI is complicated is an understatement. Machine learning, a subset of artificial intelligence, is a multifaceted process that integrates and scales mountains of data that comes in different forms from various sources. Data is used to train machine learning models in order to develop insights and solutions from newly acquired related data. For example, an image recognition model trained with several million dog and cat photos can efficiently classify a new image as either a cat or a dog.  

A better way to build and manage machine learning models

The development of machine learning models requires the coordination of many processes linked together with pipelines. Pipelines can handle data ingestion, scrubbing, and manipulation from varied sources for training and inference. Machine learning models use end-to-end pipelines to manage input and output data collection and processing.

To deal with the extraordinary growth of AI and its ever-increasing complexity, IBM created an open-source framework called CodeFlare to deal with AI’s complex pipeline requirements. CodeFlare simplifies the integration, scaling, and acceleration of complex multi-step analytics and machine learning pipelines on the cloud. Hybrid cloud deployment is one of the critical design points for CodeFlare, which using OpenShift can be easily deployed from on-premises to public clouds to edge.

 It is important to note that CodeFlare is not currently a generally available product, and IBM has yet to commit to a timeline for it becoming a product. Nevertheless, CodeFlare is available as an open-source project.  And, as an evolving project, some aspects of orchestration and automation are still work in progress. At this stage, issues can be reported through the public GitHub project. IBM invites community engagement through issue and bug reports, which will be handled on a best effort basis. View More