Assembly Required: How to Decide Whether to Build or Buy your MLOps Platform

Build vs. Buy Guide

Enterprises that are serious about investing in AI as a profitable strategy need the tools in place to scale AI and bring data science to production. Technology leaders are faced with the challenge of how to approach machine learning operationalization (MLOps) from a tool stack perspective. Should enterprises build a custom in-house MLOps framework, or use an off-the-shelf platform? 

Going the DIY route can have a lot of advantages when it comes to customization and adapting to specific use cases or organizational needs. MLOps platforms have been available in the market for the past couple of years and are gaining popularity. In this paper, we’ll explore the advantages and disadvantages of each option, for the business and its teams, and try to determine in which cases you should buy an off the shelf solution and in which cases it makes more sense to build your own platform in-house.

In this whitepaper, you’ll learn:

  • The components of an MLOps stack
  • What it takes to build one in-house, from considerations like infrastructure, to team members, to time and cost
  • When it makes sense to build an MLOps framework in-house
  • The advantages of buying an MLOps Platform off-the-shelf
  • When it makes sense to buy one off-the-shelf
  • How a one hundred year old industry leader transformed their organization with AI, by adopting an off the shelf MLOps solution.

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About Iguazio

The Iguazio Data Science Platform enables enterprises to develop, deploy and manage AI applications at scale.  With Iguazio, enterprises can run AI models in real time, deploy them anywhere (multi-cloud, VPC or on-prem), and bring to life their most ambitious data-driven strategies. Iguazio is backed by top strategic investors such as Bosch, Verizon Ventures, Samsung, CME, Group and Dell, and is partnered with dozens of technology companies including Azure, AWS, Google, NVIDIA and NetApp. 

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