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
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
- Process of launching a competition
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