This is pure comedy. Want to waste a bunch of cloud resources operationalizing a garbage model? Have engineers do it. The most successful workflow strategy I’v

This is pure comedy. Want to waste a bunch of cloud resources operationalizing a garbage model? Have engineers do it. The most successful workflow strategy I’ve seen in practice is where the deep learning researcher is also the person operationalizing the model. The same person who is grokking the latest paper in arxiv is also studying correlations in product data to perform feature engineering and also writing Dockerfiles to make the work reproducible and optimizing containers for production deployment, latency, failure tolerance, and evaluating performance in the specific context of the business application and creating well crafted software components with adequate testing along the way. The commodity part is the cloud engineering, kubernetes pod setup, load testing tools, and general software engineering. Machine learning engineers are typically great at these things and they are easy to learn. Meanwhile, learning about the nuance of hyperparameter tuning, how to evaluate overfitting, model complexity tradeoffs, when to use which kind of statistical modeling tool, how to improve models based on observing error cases, and a host of other statistical modeling concerns are wildly not commoditizable at this point of history. Knowing how to copy paste some Keras tutorials will not help you.