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Ebook

Building vs. Buying a Machine Learning Management Platform

Many organizations actively developing their machine learning (ML) capabilities struggle to extract a return on their AI investment. One of the biggest hurdles is maintaining a growing library of machine learning algorithms and environments, which often make it impossible to properly operationalize machine learning models.

However, building a machine learning management solution is a challenge in and of itself—from unexpected complexities, development issues, and management costs to the lack of internal expertise and scalability roadblocks. Purchasing an off-the-shelf solution could be an alternative that can alleviate all of these issues.

Download Building vs. Buying a Machine Learning Management Platform to learn:

  • What building a machine learning management platform entails
  • How to make a business case for ML and share it within your organization
  • What you should evaluate when looking to buy an existing ML management platform
  • Why an off-the-shelf platform might be the better option for your organization
  • Being successful with AI is very hard. It requires the right technology and that the technology be end to end. It requires a plan for how you’re going to realize value. Those who just buy an AI tool and don’t do their due diligence on what the tool does, or don’t create a plan, are not setting themselves up for success, or certainly not on the timeline they would like.
    Debanjan Saha
    Debanjan Saha

    Chief Executive Officer, DataRobot

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