End-To-End Operational ML with Dataiku and HPE Ezmeral ML Ops

Presented by

Sandeep Deshmukh (HPE), Dietrich Zinsou (HPE), Daniel Hladky (Dataiku)

About this talk

Enterprises are facing challenges with operationalizing their ML models as they move from PoCs to production. The emerging field of ML Ops – machine learning operations – aims to deliver agility and speed to the ML lifecycle similar to what DevOps processes have done for the software development lifecycle. In this webinar, we will discuss how to: - Overcome the barriers of deploying and operationalizing ML models - Gain faster time-to-value, increase productivity, and reduce risk with a flexible end-to-end ML Ops solution - Deploy and access data more efficiently whether on premises, in the cloud, or a hybrid environment Join this webinar to learn how Dataiku and HPE are bringing speed and agility to the ML lifecycle. Speakers: - Sandeep Deshmukh, Product Manager ML Ops at Hewlett Packard Enterprise - Dietrich Zinsou, Senior Solutions Architect at Hewlett Packard Enterprise - Daniel Hladky, Senior Partner Manager at Dataiku Please be aware that by registering for this webinar, you agree to have your personal information shared with both partners Dataiku and Hewlett Packard Enterprise. They may contact you with information that could be of interest to you.

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Hewlett Packard Enterprise (HPE) is transforming how enterprises deploy AI / Machine Learning (ML) and Big Data analytics. HPE’s container-based software platform makes it easier, faster, and more cost-effective for enterprises to innovate with AI / ML and Big Data technologies – either on-premises, in the public cloud, or in a hybrid architecture. With HPE, our customers can spin up containerized environments within minutes, providing their data scientists with on-demand access to the applications, data, and infrastructure they need.