QA TEST - DO NOT USE - Custom Development vs Ready-to-Use Solutions for Artificial Intelligence (Clone)

Grzegorz Mrukwa

Jun 30, 2021 • 8 min read

So you’re ready to dive into the exciting world of AI. However, in such a rapidly-evolving field it may be difficult to decide how to go about it.

What’s the best way to create a robust and effective AI product? Should you develop a custom solution or buy one off the shelf? We gathered practical pros and cons for both options to help you decide.

What is AI and how your company can benefit from it

Artificial Intelligence is a concept of machines acting with perceived intelligence. It can refer to any system in which machines can learn and solve problems based on previously-executed tasks. Using techniques like machine learning and deep learning, AI can improve with time and not just do the tasks on its own, but do them better. In other words, AI machines are programmed to have some traits of human-like thinking. That’s not to say they think like humans - we are probably still decades away from that.

AI doesn’t have to look like a human, either. For a long time, the pervasive image of AI was a humanoid robot assistant. Many tech solutions can have different AI traits, or benefit from having one implemented into their workings. AI can help optimize processes thanks to pattern recognition and natural language processing.

AI as a scientific discipline has been around since the 1950s, but has only recently become powerful and affordable enough to allow for a wide array of applications in business and technology. Some of them include:

  • Personalization - AI can help to prepare personalized products as well as pricing and promotions which raise conversion and increase sales.
  • Conversational AI - Robo-advisors, virtual assistants, and chatbots can markedly improve customer experience.
  • Predictive analytics - AI-powered algorithms give companies a competitive edge over the competition. Look no further than Netflix or Amazon for examples.

Industries as varied as retail, finance, health, transport, and security are already implementing AI solutions. This trend will shape the business world in the years to come.

How to make the best use of AI for your company? Should you create a fully-customized AI product or buy a ready-to-use solution? Let’s take a look at the different options and compare their advantages and limitations.

Custom AI development

Developing your own AI solution means you’re going to construct machine learning models from scratch. Whether you decide to do it in-house or outsource the task, this will include building custom algorithms and your proprietary API. Before you dive into the process, it’s important to assess the development costs and the value of bespoke AI you’re going to get.

Pros of Custom AI:

  • Intellectual property - You own the solution and can potentially benefit from selling it to third parties. That’s especially important if such solutions are part of your core business.
  • No extra fees - Since the software belongs to you, you won’t have to pay subscription fees or for extra data processing.
  • High quality of predictions for specialized data - If you need your software to process a highly specialized set of data, this point is crucial, as it’s less likely that there’s a ready-to-use solution that can do that. Instead, it would produce a generic, useless output. On the other hand, a bespoke AI system can offer output well-suited to your specific business problem.
  • Rigorous testing scenario - During the development of custom AI, testing will be adjusted to the specific data set and guarantee great performance.
  • More control over the product - Once the development is over, you’re not dependent on a third party provider. You decide when to update or scale the product.

Cons of Custom AI:

  • Upfront cost - The initial cost of developing your own solution is usually higher. In the long run, these costs might amortize better than in the case of an off-the-shelf AI product, but it’s still something to consider, especially if you have limited resources.
  • Time - Data input is a crucial step in developing a machine learning solution. Providing the training data that the AI can learn from is just one of the reasons why creating a bespoke AI product might take longer than deploying one developed by a third party. For example, developing a solution with Amazon Personalize, a generic recommendation engine, requires just integration of the service, while developing a custom solution requires at least a month-long engagement.
  • Infrastructure requirements - Hosting a bespoke AI product requires a significant processing capacity, necessitating investment in either physical hardware or cloud-based services capable of handling AI workloads.

Ready-to-use AI

Until recently, it wasn’t likely that you would be able to find a ready-to-use AI solution that would fit your specific needs. But as the industry matures, more and more ready-to-use AI products are becoming available. For example, if you need AI to perform natural language processing, there’s probably no need to develop it from scratch - especially if your company is small and technology is not part of its core business. Google, Amazon, Microsoft and others have already invested a lot of money and manpower into developing AI solutions that you can easily adapt for your needs.

Pros of ready-to-use AI:

  • Low development cost - The initial cost is going to be lower than in case of having a product made from scratch.
  • Quick deploy - Configuration and onboarding will take a few weeks at most.
  • High quality of predictions for generic data cases - The AI that’s on the market today has state-of-the-art prediction capabilities for generic cases. So for example, if you need your AI to recognize an image of a cat, ready-to-use AI will do just fine.
  • Hands-off management - You won’t have to bother with the maintenance of the software since this responsibility will fall on the provider.

Cons of ready-to-use AI:

  • Data customization - The machine learning platform will offer you a large amount of data. Still, you will need to augment it with your own data set if you want the output to be specific to your situation. Also, validating that the platform’s predictions are good before launch will take a lot of effort.
  • Lower quality of predictions for specific data cases - If you buy a generic AI solution, it might not be able to work with specialized problems.
  • Costs rising with system growing - Most machine learning platforms charge a subscription fee that depends on the amount of data processed. If there are several algorithms to add and additional data to train, the costs of using the platform can rise significantly.

Conclusion

Between the extremes of fully bespoke and ready-to-use AI, there are also in-between solutions which allow you to add a few custom elements to the available machine learning platforms. There is definitely no one-size-fits-all solution and many factors can influence the final decision.

Are you unsure which way to go? Netguru has years of expertise building both custom AI solutions and helping companies deploy commercial off-the-shelf machine learning products. We offer a free personalized consultation, which will enable us to assess your company’s specific requirements and provide you with all the tools you need to make the best use of AI.

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Grzegorz Mrukwa

Data Science Manager
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