Contributed content from Foutse Khomh and Giuliano Antoniol, Professors in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal (Canada), Montreal, Canada.
Red Hat, as an open source community leader, participated with AI/ML researchers at the Software Engineering for Machine Learning Applications (SEMLA) initiative – sharing how organizations can take advantage of modern infrastructure based on open technology such as agile integration, microservices, and containerized applications – to help build and deploy managed, scalable intelligent applications on hybrid clouds.
Organizations eager to adopt AI and machine learning (ML) are up against significant challenges. The practice of bridging the gap between data science and operations, much in the same way that DevOps can for application development. And just as with DevOps, there are architectural, cultural and process considerations associated with creating an agile AI/ML environment. For example, parallel to modern DevOps, open hybrid cloud platforms can allow for faster turnaround of refining models, integrating disparate data sources more quickly, and making it easier to take advantage of ML capabilities and tools from both providers and third parties – who have made their solutions available as containerized services.
Continue reading “Bringing AI and machine learning data science into operation”