Automating ML Workflow with IBM’s Fabric for Deep Learning (FfDL)

Cloud environments provide a lot of benefits for advanced ML development and training including on-demand access to CPUs/GPUs, storage, memory, networking, and security. They also enable distributed training and scalable serving of ML models. However, training ML models in a cloud environment requires a highly customized system that links these different components and services together and allows for managing and consistently orchestrating ML pipelines. Managing a full ML workflow, from data preparation to deployment, is often really hard in a …

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