The doctor would enter her chamber and find that the patient was hidden behind an opaque bed curtain. It’s a bit like the stories from ancient China in which a learned physician was called in to diagnose an aristocratic female patient. The challenge of safely getting outside help is basically the same whether the project involves sophisticated machine learning or much more basic data analytics and engineering.
You may want to get help via collaboration with machine learning experts, but how do you do so effectively when they cannot be allowed to see sensitive data stored behind a security perimeter?īusiness analysts, data scientists, and data modelers all face this type of problem. The problem: secure development can be difficult. In these situations, the project can often benefit from the experience of outside collaborators, but getting this help can be challenging when there is a security perimeter protecting the system, as suggested in Figure 4-1.įigure 4-1. The data scientist must be able to interpret the initial results produced by the trained model and use this insight to tweak the knobs of the right algorithm to improve performance and better model reality.
It’s often not a straightforward cookbook process, but instead one that requires the data scientist to have a good understanding of the data. Machine learning requires an iterative approach that involves training a model, evaluating performance, tuning the model, and trying the process over again. What’s the solution?Ī similar problem arises when someone is trying to do secure development of a machine learning system. Their data and the details of their project may indeed be too sensitive to be shared with outside experts like you, and yet they do need help. Can we log into your machine?Īt first glance, it may seem as though the customer in this scenario is being unreasonable, but they’re actually being smart and responsible. Can you help us?Įxperts: We’d be happy to. Customer: We have sensitive data and a critical query that doesn’t run at scale.
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