5 Best Practices for Putting Machine Learning Models Into Production

5 Best Practices for Putting Machine Learning Models Into Production

5 Best Practices for Putting Machine Learning Models Into Production. Our focus for this piece is to establish the best practices that make an ML project successful.

In our previous article – 5 Challenges to be prepared for while scaling ML models, we discussed the top five challenges in productionizing scalable Machine Learning (ML) models. Our focus for this piece is to establish the best practices that make an ML project successful.

ML models today solve a wide variety of specific business challenges across industries. The method of choosing an ML model largely depends on the business use case that we are trying to solve. But before proceeding any further, we should ensure that the chosen approach to build the models are produtionizable.

Sigmoid’s pre-webinar poll showed that 43% of companies find ML productionizing and integration challenging.

Due to the complexities, the right risks have to be eliminated early off in the production process. Eliminating a higher number of risks at earlier Stages of the model selection & development leads to lesser rework during the productionizing stage.

The various considerations involved in a machine learning ecosystem are — data sets, a technology stack, implementation and integrating these two, and teams who deploy the ML models. Then come the resilient testing framework to ensure consistent business results.

_Using the best practices given below Yum! Brands were able to achieve an 8% sales uptick by productionizing their MAB models for personalised email marketing. _Watch the 2 min video where Yum’s Scott Kasper explains the impact of the best practices in productionizing their MAB models

2020 oct tutorials overviews best practices machine learning production

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