##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
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.
Anthill Inside 2019 sponsors:
#Bronze Sponsor
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#Community Sponsor
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Hosted by
Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
PN
Puneeth N
@puneethnarayana
Taking AI Products to Market
Submitted Apr 30, 2019
Section:
Birds Of Feather (BOF) session
Technical level:
Intermediate
Session type:
Lecture
Want to have specific focussed discussion on what it takes to take pure play AI products to market. Have first hand experience taking multiple AI products to Indian B2B Market, I wanted to get product managers and KBLs in the domain to come in and share their journey and experiences. Hence, suggesting a bird of feather session.
Session will shed light over all stages of the AI product development and how it differs from regular software developement.
Outline
Right from conceptualization, to data collection, annotation, repeated model tweaking, and model deployments.
Handling pure play AI and Data Science products has it’s own life cycle.
I want to share 2 such experiences that I have had over the past 3 years.
Outline
- What constitutes a AI product?
- Steps of Creating an AI product
- Learning to Swim, what happens when you put the product in the field.
- Lessons from the Indian Market
- Dealing with various stakeholders
- Key Takeaways
Speaker bio
Puneeth is a quintessential technologist, with a zest for converting awesome innovations into great products. Having started off his career at Accenture Tech Labs over 5 years ago, Puneeth has focussed exclusively on products related to data domains and technologies. Through his career, he has donned various hats as a Data Engineer, Scientist, BI Consultant, AI Product Analyst, Co-founder and Product Manager. His most recent engagement has been to help SigTuple scale their flagship AI product in diagnostics.
Puneeth has a Masters degree in Computer Science from IIIT Bangalore.
Links
Slides
https://docs.google.com/presentation/d/1lfEngm1CsCqzC9DhbziJCX8vZTek-gXHu_h_Gk6Iyz4/edit?usp=sharing
Comments
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Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
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