IBM introduced CodeFlare at the Ray Summit in June of 2021. The platform was introduced to drastically reduce the time required to set up, run, and scale machine-learning tests. For example, CodeFlare reduced the time to execute each pipeline from 4 hours to 15 minutes when one user used the framework to examine and improve approximately 100,000 pipelines for training machine learning mode. Recently, IBM announced that CodeFlare significantly reduces the time to automate transfer learning tasks for foundation models. CodeFlare is a hybrid multi-cloud platform that streamlines the integration, scalability, and acceleration of complicated multi-step analytics and machine learning pipelines.

IBM Wednesday announced CodeFlare, an open-source, serverless framework designed to simplify the integration and efficient scaling of big data and AI workflows onto the hybrid cloud. CodeFlare is built on top of an emerging open-source distributed computing framework for machine learning applications known as Ray. IBM said CodeFlare extends the capabilities of Ray by adding specific elements to make scaling workflows easier. With data and machine learning analytics are proliferating into just about every industry, tasks are becoming far more complex, IBM noted. While it is important to have larger datasets and more systems designed for AI research, as these workflows become more involved, researchers are spending more and more time configuring their setups than getting data science done.

There is a natural synergy (yes, we're using that word) among the many subcategories that make up the AI world. It would be impossible to talk about synthetic data without talking about machine learning, computer vision, software, ethics, privacy (and neural rendering, and GANs, and our Marketing and Sales director Michael's daughter's book Neural Networks for Babies) -- so that's why we don't do that. But synthetic data remains the apple of our eye. So we were thrilled to discover that Gartner Inc.'s June Report predicts that by 2030, the most used type of data in AI will be synthetic. Modernization can be a tricky thing, especially when it requires industry-wide adjustments.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. IBM today announced a new serverless framework called CodeFlare that's designed to reduce the time developers spend preparing AI models for deployment in hybrid cloud environments. The company says it automates the training, processing, and scaling of models to enable engineers to focus on data insights. Data and machine learning analytics are proliferating across industries, with the tasks becoming increasingly complex.