Nov 2019
18 Mon
19 Tue
20 Wed
21 Thu
22 Fri
23 Sat 08:30 AM – 05:30 PM IST
24 Sun
Make a submission
Accepting submissions till 01 Nov 2019, 04:20 PM
##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com
#Sponsors:
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
#Bronze Sponsor
#Community Sponsor
Hosted by
Nilesh Patil
@nilesh_patil
Submitted Apr 30, 2019
In this talk, we propose a way to minimize the effort required to move data munging, visualization and training for ML models into production. Python notebooks are a go-to tools to carry out such research. Moving from this painstaking research to a full blown deployment requires too much effort and care from a data scientist’s perspective. Attempting to make the modeling process amiable to production pipeline or keep redoing the complete process as and when required is a drain on your time and resources.
The interactive nature of this approach makes it easier to visualize and debug the model performance. As a data scientist, you end up possessing the ability to approve/reject changes in the model/dashboard after each data refresh - with minimal effort.
In this one hour session we tackle the problem by treating notebooks as an integral part of our production pipeline.
We propose an approach, where we integrate the modeling, visualization and product deployment steps with standard Jupyter Notebooks and Dash.
Laptop, Anaconda with python3.7 environment
We are part of the datascience team at Dream11 - India’s largest fantasy sports platform and have to rapidly iterate through different projects and ideas on a regular basis. We have recently moved from academic machine learning (read : research related ad-hoc projects) to being stake holders in the data science production pipelines @Dream11.
We present the approach our team has taken to reduce our effort and increase control over model deployment by leveraging jupyter notebooks+dash to provide an interactive ML training, analysis and deployment experience.
Hosted by
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}