Abstract: 

There are very few open-source tutorials/resources that cover the end-to-end of what is required to build an enterprise level deep learning pipeline for Computer Vision related solutions.

There are multiple components that go into building an end-to-end solution for Computer Vision. All these are already available as open-source projects but are disparate and require an expert to leverage them well.

This tutorial aims to bring together all such components and make them work together as we build and end-to-end pipeline which the audience can use for their organization's Computer Vision related projects.

The components that we will discuss:
* Interactive Ground Truth Data Annotation
* Model training
* Building lighter models for fast inference
* Metrics visualization
* Pilot phase
* Deployment

The tutorial will cover the above aspects as we demonstrate live on a particular project to the participants.

All participants will have access to the code/notebooks that will be discussed during the presentation so that they can leverage them offline.

Session Outline
* Interactive Ground Truth Data Annotation
* Model training
* Building lighter models for fast inference
* Metrics visualization
* Pilot phase
* Deployment

Background Knowledge
Python and familiarity with Machine Learning Life Cycle

Bio: 

Kishore is a hands-on leader with deep interest in leveraging Technology, Data & Machine Learning to identify, communicate & solve Business problems - essentially, Applied Data Science.

He has worked across Healthcare, Retail, eCommerce, Financial Services. Established, grew applied data science teams for more than a decade since his MBA in IT & Operations from IIM Calcutta.

In this process, he has filed 11 patents at the intersection of Machine Learning, Healthcare, and Operations & has written 4 books on ML & Deep Learning.