Anthill Inside 2019

A conference on AI and Deep Learning

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Submissions are closed for this project

Taj M G Road, Bangalore, Bangalore

About the 2019 edition:

The schedule for the 2019 edition is published here:

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


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

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

Nilesh Patil


Bridging the gap between research to the deployment of Machine Learning models

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.

  1. Import data (sample dataset to provide a hands on experience)
  2. Data munging (mostly pandas and base python)
  3. Visualize different aspects of the dataset
  4. Train and save ML model (scikit-learn/pytorch/fastai based)
  5. Provide a interactive model deployement process combining jupyter notebooks+dash


Laptop, Anaconda with python3.7 environment

Speaker bio

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.



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Abhijith C

Modeling the effects of blurriness in mobile ads

The creative is the image of the ad that the user sees and engages with upon viewing. This talk studies the effect of an ad creative’s specifications and quality of render in performance campaigns and suggests a playbook for digital marketers based on the findings and insights. An offline study on an ad creative’s specifications such as resolution, aspect ratio, handset density, device orientatio… more

30 Apr 2019