Accelerate innovation with DevOps-like agility for machine learning pipelines
You need to be able to accelerate innovation and deliver faster time to value for your AI initiative while ensuring enterprise grade security and high performance. You need to provide easy access to the tools and data your data science teams need for large-scale distributed ML and DL with greater agility for rapid prototyping, iteration, and deployment.
Nanda Vijaydev dives into examples of—and lessons from—ML and DL use cases in financial services, healthcare, and other industries. You’ll learn how to quickly spin up containerized multinode environments for TensorFlow and other ML and DL tools—to train models in a multitenant architecture on-premises, in the cloud, or in a hybrid environment. She also explores the challenges and best practices regarding ML operationalization and model deployment in production.
Hewlett Packard Enterprise
Nanda Vijaydev is the lead data scientist and distinguished technologist at BlueData (now HPE), where she leverages technologies like TensorFlow, H2O, and Spark to build solutions for enterprise machine learning and deep learning use cases. Nanda has more than 10 years of experience in data science and data management. Previously, she worked on data science projects in multiple industries as a principal solutions architect at Silicon Valley Data Science. She also served as director of solutions engineering at Karmasphere.
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