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Advantages of using bespoke.
Accuracy is often touted as the reason for using a bespoke model, but as discussed above OTS models can outperform a bespoke model if your use case fits. Often OTS doesn’t fit well and has to be shoe-horned, this can be ok for a proof of concept, but a bespoke model will quickly outperform in this scenario.
With bespoke models, you are able to be more flexible and apply any cutting-edge techniques that might become available a lot sooner than waiting to see if your service provider will implement any new techniques. With this flexibility also comes the ability to experiment and try different ways of solving your specific problem.
With a bespoke model, you get more control over the privacy of your model, as the data is not being passed through your service providers system and therefore you don’t need to be concerned about their stance on privacy.
Disadvantages of using bespoke.
Building truly bespoke machine learning models can be hard, very hard. Especially when it comes to feature engineering, optimisation, and productionisation, there is a lot to consider. Most machine learning systems require a subject matter expert and a machine learning expert, and as with all inter-disciplinary activities, it comes with unique challenges.
Another consideration once the model is in production is infrastructure and maintenance. Using appropriate infrastructure to work with your customers, end users, or platform development team can be a challenge as you can’t just push a jupyter notebook into production.
If your model becomes a success, you then need to work out how to scale it. Planning scalability into models from the outset takes prior planning and no scope drift. Often projects result in a model rebuild at some point to allow for scalability at a later date.
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