Global tech giant IBM had announced that its machine learning framework, CodeFlare, will now be available as open-source to make the training process of models more efficient.
With the increase in the application of Data analytics and machine-learning across industries, the dataset required to train the machines has also increased simultaneously. It has made the training process more complex and time-consuming.
Even it is more difficult for researchers to focus on data science between time-consuming actions to keep their systems updated.
IBM CodeFlare will allow developers to train the Artificial Intelligence-based models in less amount of time onto the hybrid cloud. This open-source platform will interest the developers who want to spend less time on training by simplifying their workflow by leveraging the framework.
This framework has been designed in such a way so that an implementation of 10,000 work pipelines on the machines can be done in only 15 minutes which used to take a user about 4 hours to see the results.
Training and cleaning of data are essential steps of a machine learning process to receive the best results possible.
IBM CodeFlare will allow data scientists to access different platforms and frameworks without worrying about learning new languages.
IBM Cloud Code Engine will be the serverless platform used to execute CodeFlare pipelines efficiently. The benefit of this serverless platform is that it allows data scientists and AI researchers to deploy from anywhere.
Even it will also make integration with the other cloud-native ecosystems more convenient. Data received from cloud object storage, distributed filesystems, and data lakes can be used to trigger events.
IBM CloudFlare will enable data scientists to focus more on innovation and analysis by reducing the complexity in their domain. With IBM sparing data scientists from the deployment and configuration challenges, it allows them to conduct their research work more deeply by utilizing the rich set of tools this platform offers.