Machine Learning as a Service: How to deploy ML Models as APIs without going nuts
Anand Chitipothu (~anandology) |
Often, the most convenient way to deploy a machine model is an API. It allows accessing it from various programming environments and also decouples the development and deployment of the models from its use.
In this talk demonstrates how deploying machine learning models an APIs can be made fun by using right programming abstractions.
The talk presents the couple of open-source libraries firefly and rorolite created to solve this very problem and also shares the experience of building cloud-based PaaS platform that addresses these issues.
The participants should have understanding of machine learning models and APIs.
Anand has been crafting beautiful software since a decade and half. He’s now building a data science platform, rorodata, which he recently co-founded. He regularly conducts advanced programming courses through Pipal Academy. He is co-author of web.py, a micro web framework in Python. He has worked at Strand Life Sciences and Internet Archive.