Machine Learning as a Service: How to deploy ML Models as APIs without going nuts

Anand Chitipothu (~anandology)


3

Votes

Description:

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.

However, building an good API is hard. It involves many nitty-gritties and many of them need to repeated everytime an API is built. Also, it is very important to have a client library so that the API can be easily accessed. If you every plan to use it from Javascript directly, then you need to worry about cross-origin-resource-sharing etc. All things add up and building APIs for machine very tedious.

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.

Prerequisites:

The participants should have understanding of machine learning models and APIs.

Speaker Info:

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.

Section: Data science
Type: Talks
Target Audience: Intermediate
Last Updated:

Looking forward for this talk.

Wasim Thabraze (~waseem18)

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