##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 firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Building Products with ML: A Workshop for Product & Engg Managers
Machine learning (ML) has seen substantial adoption, and a large number of data
science teams are being created. Taking on ML projects requires product managers
and engineers to learn an ML approach to problem solving, to be able to effectively
work with data scientists and data engineers. There exists a huge gap in understanding
- both cultural & technical - in most organizations since product owners and the
engineering teams have not worked with ML before. The ML approach is sufficiently
different from a usual software engineering approach that it needs deeper
understanding and adoption
The aim of this workshop is to acquaint product managers and engineering managers
with ML ways, to effectively lead ML teams producing outcomes of value.
The workshop starts with an introduction to ML to get an intuitive understanding of ML algorithms work,
and covers the typical project lifecycle and roles and responsibilities. It also covers
common mistakes while executing ML products followed by best practices & design
patterns from an ML software perspective. Finally, there is a brief session on ML data
governance challenges. Multiple case studies are discussed to illustrate these concepts,
along with interactive sessions to practice applying the content to custom usecases.
Should have 5+ years experience in the Industry. Preferably people who
have worked as a Product Manager / Engineering Manager before.
Lavanya Sita Tekumalla is a Machine Learning Scientist and the founder of AiFonic Labs. She holds a PhD in Machine Learning (Bayesian Models) from the Indian Institute of Science and a Masters in Computer Graphics from the Uninversity of Utah. She has been in the Industry for over 9 years in various roles (Ex-Amazon-twice, InMobi, Myntra, Kenome). She has also owned product and helped with ML at Kenome, a deep-tech startup until recently.