Anthill Inside 2019

A conference on AI and Deep Learning

Make a submission

Accepting submissions till 01 Nov 2019, 04:20 PM

Taj M G Road, Bangalore, Bangalore

About the 2019 edition:

The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule

The conference has three tracks:

  1. Talks in the main conference hall track
  2. Poster sessions featuring novel ideas and projects in the poster session track
  3. 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:

  1. Data scientists
  2. AI, DL and ML engineers
  3. Cloud providers
  4. Companies which make tooling for AI, ML and Deep Learning
  5. 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

iMerit Impetus

Community Sponsor

GO-JEK iPropal
LightSpeed Semantics3
Google Tact.AI
Amex

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

Bikram Sengupta

@bikramsengupta

Build an enterprise grade data labelling pipeline to scale your ML/AI pipelines

Submitted Nov 7, 2019

In Software 2.0, Data is code. A mindful approach to your data annotation pipeline and practices is critical to the outcomes of your ML algorithms. If not done right, your ability to scale this pipeline can often prove to be a major blocker to productionization.

In this talk we focus on why and how to build your data labeling pipeline to be enterprise grade. We will describe the considerations and insights that go into making your data pipeline a mindful part of your development pipeline, so that you can follow the journey from PoC to production. We describe best practices and provide pointers to designing a high quality, iterative, and scalable data annotation practice.

A pipeline designed for human judgement and incremental training on edge cases, can provide that last mile of acceptability to roll out a machine learning solution in production. We will describe successful examples of this approach.

Outline

To be added

Speaker bio

Bikram is broadly interested in the role of AI in addressing challenges and improving outcomes in education, skill development and employability. At iMerit, Bikram heads innovation with a primary focus on designing tech-enabled learning experiences that enable iMerit’s workforce – sourced largely from impact communities - develop digital economy skills and deliver high quality data annotation services to AI organizations. Bikram is also CTO of Anudip Foundation, iMerit’s not-for-profit sister organization that trains youth from underserved communities in new age digital skills and helps them find gainful employment. Prior to joining iMerit and Anudip, Bikram spent 15 years at IBM Research, establishing and leading global R&D programs in educational technologies, service delivery, and software engineering. Bikram holds MS and PhD degrees in Computer Science from the State University of New York, Stony Brook. He has co-authored more than 50 scientific publications in international journals and conferences, and holds several US patents.

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Make a submission

Accepting submissions till 01 Nov 2019, 04:20 PM

Taj M G Road, Bangalore, Bangalore

Hosted by

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more