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The discussion revolves around the importance of choosing the right model in data science and whether clients expect detailed reports on the variants and iterations of the models used.
If the client has a data science team or chief data scientist, they might expect comprehensive reports on algorithms used, hyperparameter tuning, and results achieved. For sensitive projects like employee attrition models, clients might outsource to prevent leaks of sensitive predictions.
In many cases, clients are primarily interested in the business outcomes of a model rather than the specifics of its development. However, documentation is vital to support claims about a model’s accuracy and to replicate results.
Cloud-based AutoML techniques automatically experiment with numerous models, and they document all experiments performed, making the documentation process simpler.
Deployment of an ML model considers hardware, the ML code (software), and the data used. Assessing deployment needs combines the expertise of data scientists and the IT team. The performance of the model heavily relies on the chosen hardware, and it can be deployed on local or cloud machines.
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