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Machine Learning Container on PowerEdge MX and VMware Cloud Foundation 4.0 with Tanzu

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Executive Summary
Introduction A Collaborative Partnership Machine Learning Complexity

Introduction

  • This document outlines the reference architecture for two functional machine Learning (ML) retail use cases. It was developed on VMware Cloud Foundation 4.0 with Tanzu built on Dell EMC PowerEdge MX7000 Kinetic Infrastructure.

    This paper and reference architecture represents a collaborative effort to design, build, and support a functional machine learning (ML) and data science platform as an on-premise deployment.  Utilizing the industry leading vSphere 7 with Tanzu framework drastically cut down the complexity often found within a machine learning framework.

    The experience from the extended team ensured the design, deployment, and implementation of fully functional use cases that incorporated hardware (Dell Technologies), software (VMware), and unique application source code and engineering services (Grid Dynamics®). It covers typical business problems, IT operations complexity, and potential pitfalls that you will face while building your machine learning or data science platform - and provides guidance and solutions to help you overcome and avoid many of those issues, saving time and effort. 

    This Reference Architecture provides a foundation for those looking to:

    • Migrate an existing data platform based on open-source technologies
    • Use open-source and proprietary ML technologies together
    • Implement a new cloud-native VMware container platform on-premise
    • Implement a specific capability on a cloud-native stack

    This paper and functional demonstration represent a modern and dynamic deployment effort between Dell Technologies, VMware, and Grid Dynamics®, a commissioned engineering IT Services company known for innovation in the retail, finance, and technology sectors.