Avoiding the Anti-Pattern in Analytics: Three Keys for Machine Learning Success

When a different analytic model is used in training versus deployment, results can be disastrous. Here’s how to avoid the anti-pattern.I recently stumbled over a great slide deck presented by Netflix engineers about “Design Patterns for Real World Machine Learning Systems.” The presentation warns of a dangerous “anti-pattern” when applying analytic models to production systems. The anti-pattern basically involves using different platforms, tools and technologies to develop and deploy an analytic model. That is, developers use different code, data, and platforms for training an analytic then deploying it.  Unfortunately, many enterprises actually do this in their projects, and it can be disastrous: Machine-learning analytics are deployed in use cases such as predictive maintenance, cross selling, customer churn, fraud detection or sentiment analysis, to name just a few examples. Anatomy of…


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