Containerization of ML Workloads to Increase Data Science Productivity


One of the challenges with Machine Learning and Data Science projects is standardizing compute environments so that teams will start with approved packages and libraries that are functional together. Cloudera has developed a pluggable approach to this which gives users access to a blueprint to create custom ML Runtime that consists of not just language specific libraries, but also a compute kernel or OS and partner content. Cloudera is open sourcing these ML Runtimes as a way to promote tighter integration with partners and to make it easier for customers to start from their own approved images.


Chris is currently working in product management at Cloudera for their ML products where he is focused on bringing full life cycle machine learning to the public cloud providers by leveraging the Cloudera Data Platform. He is also a former Principal Engineer at Cloudera. Chris is a graduate of Texas A&M University and spends his personal time on mountain trails in his jeep.

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