Conclusion

Taking data science algorithms from experimentation to large-scale production requires an end-to-end ML engineering process in place to maintain and manage an Enterprise AI system. This becomes a more involved process for scaling DL models, as in the use cases described here. This whitepaper describes an approach to scale ML and DL models in production and reduce the time from “ideation to production”, while minimizing cost and maximizing gains for the organization.