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.
Anthill Inside 2019 sponsors:
Production Object Detection - A Journey of Training, Building and Deploying CV models
Computer Vision as a field has changed manifold in the past few years. Researchers publish their papers and at times their code for the latest algorithms, but the challenge for the industry remains in applying that research to their processes.
Customising a company’s proprietary data for the research models, implementing their code, and training models is the first big hurdle. Then comes the part where we have to test and release these latest models to production.
In this talk we will go through a project where we did exactly the above at Here Technologies. The audience will learn abot the main issues we faced, how we overcame it and other best practises, including optimising AWS infrastructure for Machine Learning DevOps.
- Overview of object detection approaches
- Training Data Prep - Including handling data on the cloud
- Data collection - Sampling
- Annotation and review approaches - human, automated
- Actually training the model - hardware/cloud/best practices
- Troubles with large data sets, how to deal with issues when you hit the limit of state of the art hardware
- Evaluation of the model results
- Double checking the evaluation - blind test dataset
- Release and integration with systems
- Deployment and Infrastructure
None as such. For the talk, the only pre req is basic knowledge of machine learning terminology.
I’m an engineer involved in computer vision and robotics since 5+ years. I have worked on various computer vision and data science projects including an autonomous soccer playing humanoid(acyut.com), OCR(text extraction/transcription) and object detection models. As a computer vision and data science practitioner who has faced and overcome challenges in production systems, it would be great to share some of that knowledge for the benefit of the community.