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Whitepaper 

Building versus buying an ML management platform

According to Gartner, 85 percent of all AI projects fail, and the majority of organizations actively developing a machine learning capability are struggling to extract a return on their AI investment.

Therefore it is crucial to know up front what to expect in terms of infrastructural requirements, developer workloads, time, and costs associated with building an in-house machine learning management platform so you can prepare to meet your goals.

To extract value as soon as possible from AI and maintain a competitive advantage in your industry, purchasing an off-the-shelf platform that fits into your existing workflow is the best answer.

Let us show you why.

Use this whitepaper to:

Building versus buying an ML management platform

According to Gartner, 85 percent of all AI projects fail, and the majority of organizations actively developing a machine learning capability are struggling to extract a return on their AI investment.

Therefore it is crucial to know up front what to expect in terms of infrastructural requirements, developer workloads, time, and costs associated with building an in-house machine learning management platform so you can prepare to meet your goals.

To extract value as soon as possible from AI and maintain a competitive advantage in your industry, purchasing an off-the-shelf platform that fits into your existing workflow is the best answer.

Let us show you why.

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Use this whitepaper to:

  • Understand the benefits and costs of building and buying a machine learning operations platform. 
  • See which technical resources you need to maintain an ongoing machine learning lifecycle.
  • Be a champion of enterprise machine learning's transformational capabilities at your organization.