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Artificial intelligence models

Picking the right training option for your use case.

Choosing between pre-trained or custom-trained AI models can be rather daunting if you’re not completely familiar with the ins and outs of machine learning. Let’s lay the pros and cons out for you.

Compare training options

What is right for you?

Pre-trained models are created by large AI organisations to solve a similar problem as yourself. On the other hand, custom-trained models take the data that you have in-house to create AI models that cater to your specific problem.

Pros
  • No need for gathering, structuring, labeling and organizing data
  • Can immediately be leveraged to create value, without a lot of data
  • No training expenses
Cons
  • (Almost) no customization possible
  • Costly to use in production at scale
  • Unable to control the bias that the model may have
Pros
  • Tailor-made to solve a specific problem
  • Relatively high control over the output of the model
  • Can be re-trained as time passes by to increase accuracy
Cons
  • A large amount of structured, labeled data is needed, which requires manual work
  • Outcomes can be disappointing when no correlations are found
  • Extensive infrastructure is essential to train and re-train over time
Getting started with AI The possibilities of pre-trained models for your use-cases

Ready to explore what would work for you? Read our whitepaper on pre-trained solutions.

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    Technology we use

    Pre-trained models

    Pre-trained models allow you to get started with AI almost immediately. There are several pre-trained models available on the market, with some even having the option to fine-tune them to your needs.

    Custom-trained models

    If you have a well-maintained, well-structured data architecture available, you can leverage the power of custom-trained AI models to solve specific, context-dependent problems for your organisation. When the data is available, the options are seemingly endless.

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