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Step by Step Guide to Develop Custom AI Solutions

Modern AI solutions need a degree of specialization since they are based on data. Since it takes significant effort to obtain the data and build a high performing model, there are still numerous areas where mature AI solutions do not exist. This makes companies turn to custom AI development as a solution. We explained in detail how companies can develop custom AI solutions, who they can partner with and when they should build the solutions themselves.


 

Table of contents

Introduction Custom AI Development
  • Types of Custom AI Development
  • Cases where custom AI solution > off-the-shelf solutions
  • Common areas for custom AI development
  • Vendor Landscape for custom AI development
In-house vs Outsource Development
  • Understand pros and cons of in-house solutions
  • Understand advantages of buying an AI solution
  • SMEs or mid-market non-tech companies should stay clear of in-house AI solutions
  • Large non-tech companies or mid-market tech companies may choose to build some AI solutions
  • Largest companies focused on tech need to build their own solutions
  • When should you build your own AI solution in-house?
    • Discover alternatives to in-house solution even in this scenario
    • Life savers as you implement your in-house AI solution
Alternative to in-house AI development: AI Crowdsourcing
  • What is crowdsourcing?
  • Crowdsourcing use cases in AI development
  • Benefits of crowdsourcing
  • Companies to find crowdsourced AI talent
Alternative to in-house AI development: Data Science Competition
  • Preferable solutions for competition
  • Benefits
  • Challenges
  • Process of launching a competition
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