Deep learning workloads are compute intensive, and training these type of models is better done with specialized hardware like GPUs. Luciano Resende outlines a pattern for building deep learning models using the Jupyter Notebook’s interactive development in commodity hardware and leveraging platforms and services such as Fabric for Deep Learning (FfDL) for cost-effective full dataset training of deep learning models.
This session is sponsored by IBM Watson.
Luciano Resende is a senior technical staff manager (STSM) and open source data science and AI platform architect at IBM CODAIT (formerly Spark Technology Center). He’s a member of ASF, where he’s been contributing to open source for over 10 years. He contributes to various big data-related Apache projects around the Apache Spark ecosystem as well as Jupyter ecosystem projects, building a scalable, secure, and flexible enterprise data science platform.
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