by Anthill Inside

Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

Anthill Inside 2019

Anthill Inside 2019

On infrastructure for AI and ML: from managing training data to data storage, cloud strategy and costs of developing ML models

by Anthill Inside


24 Jul 2019, Bangalore



NIMHANS Convention Centre, Bangalore

Call for proposals


Anthill Inside 2019 is a single track conference. Birds of Feather (BOF) sessions, round table discussions and office hours with speakers will be held in parallel with talks in the main auditorium.

We are accepting proposals for:

  1. Full-length (40 min) and crisp (20 min) talks.
  2. Birds of Feather (BOF) sessions – of 1 hour duration – on focussed topics.
  3. Tutorials, explaining core concepts in DL, ML and AI. Tutorials are of 1.5 hours to 2 hours duration.
  4. Hands-on workshops on Machine Learning, statistics, modelling, deep learning, NLP and Computer Vision.

Audience at Anthill Inside:

The audience at Anthill Inside will consist of:

  1. Senior AI engineers.
  2. Architects.
  3. Product managers.
  4. Product engineers.
  5. Senior data scientists.
  6. Founders and key decision makers from startups, mid-sized organizations and enterprises who are solving problems/facing challenges around running/managing infrastructure for AI and ML.
  7. Providers of managed services such as AWS, Google Cloud and Azure.
  8. GPU and CPU solution providers such as NVidia, Intel and others.
  9. Former founders of AI and ML startups who have had exits. They will share experiences of CapEx and OpEx of AI.

Topics for submitting talks:

  1. Share experience stories and case studies of data acquisition
    1.1 What are the sources from which you acquire training data? For example, how did you solve the cold start problem in your domain?
    1.2 How did you manage acquisition of life-cycle data: did you acquire the data internally and labelled the data; or, did you acquire the data from external sources and labelled the data internally; or did you acquire externally labelled data? What were the challenges with storing data in each case?

  2. Data storage case studies: we are specifically interested in hearing about:
    2.1 What do you do with data that has lost its currency?
    2.2. How do you deal with privacy issues for vast amounts of irrelevant data?

  3. Tools for AI, ML and Deep Learning. Here, we want to hear about:
    3.1 Whether you use third-party tools? If yes, why?
    3.2 Do you adopt and retrofit existing tools? Again, why? Give us a detailed case study.
    3.3 Do you develop tools for AI/ML/DL in-house? Why are in-house tools necessary for your case?

  4. Storing data on the cloud and cloud strategy.
    4.1 Do you have a multi-cloud strategy? Share this with the community.
    4.2 How do you deal with lock-in situations with single providers?

  5. GPU versus CPU – when you do use either and why? How do the strengths and limitations of each play out for your use case?

  6. Cost of developing ML models: is there a quantifiable way of doing this?

  7. CapEx and OpEx for AI – have you worked on this? Share your insights with the community.

Tutorials and Workshops:

In 2019 edition, we are introducing a day (July 23rd) of tutorials (90 - 120 mins) where facilitators will cover topics in detail. Unlike a talk, tutorials has to be interactive. Tutorials could include hands-on coding provided it doesn’t involve too much time for set up. Other than hands-on coding, it could include other hands-on activities as well. We will be also doing workshops leading up to the conference and the weekend after conference. For tutorials and workshop, we will consider any topic, provided that the proposal makes a strong argument that the tutorial / workshop is important for the Anthill Inside community.

Anthill Inside’s speaking policies:

We only accept one speaker per talk. This is non-negotiable. Workshops or tutorials may have more than one instructor.

Selection criteria:

The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:

  1. If the technology or solution is proprietary, and you want to speak about your propritary solution to make a pitch to the audience, you should pick up sponsored session. This involves paying for the speaking slot. Write to
  2. If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
  3. If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.

The criteria for selecting proposals, in the order of importance, are:

  1. Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
  2. Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual decription – will help us understand the focus of the talk, and the clarity of your thought process.
  3. Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.

No one submits the perfect proposal in the first instance. We therefore encourage you to:

  1. Submit your proposal early so that we have more time to iterate if the proposal has potential.
  2. If you have doubts about the evaluation process or want advice on the topic for submission, write to

Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.

How to submit a proposal (and increase your chances of getting selected):

The following guidelines will help you in submitting a proposal:

  1. Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
  2. The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
  3. Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks, from some of HasGeek’s other conferences, which participants liked most:
  4. We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: to structure your proposal.
  5. Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to:
  6. Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.

To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.

Passes and honorarium for speakers:

We pay an honararium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. Anthill Inside is funded through ticket purchases and sponsorships; travel grant budgets vary.

Last date for submitting proposals is 30 April 2019.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-recorded, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond the solution you have built, or your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at previous editions of Anthill Inside.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and a document showing the full workshop plan.

Contact details:

For information about the conference, sponsorships and tickets contact or call 7676332020. For queries on talk submissions, write to

Propose a session

All proposals

Weaponising Artificial Intelligence In Cyber Security
 - The Next Age of Cyber Security Endgame

Vanshit Malhotra (@vanshit)

  • Crisp talk
  • Intermediate
  • Fri, 19 Apr

AgentBuddy: Leveraging Bandit Algorithms for a human-in-loop system for Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)

Hrishi Ganu (@blah)

  • Full talk
  • Intermediate
  • Wed, 17 Apr

Exploring the un-conventional: End-to-End learning architectures for automatic speech recognition

Vikram Vij (@vikramvij)

  • Full talk
  • Intermediate
  • Sun, 17 Mar
  • slideshow

Essential Python Recipes for Deep Learning

Aakash N S (@aakashns)

  • Crisp talk
  • Beginner
  • Tue, 26 Feb
  • play_arrow
  • slideshow

The Deep Learning Showdown: How to pick the right tool for the job?

Aiko Klostermann (@aikoklostermann)

  • Full talk
  • Intermediate
  • Fri, 1 Feb

Production Object Detection - A Journey of Training, Building and Deploying CV models

Tarang Shah (@tarang27)

  • Crisp talk
  • Beginner
  • Sat, 20 Oct

Virtual Assistant for High Volume Recruitment

Piyush Makhija (@piyushmakhija)

  • Full talk
  • Intermediate
  • Mon, 15 Oct
  • play_arrow
  • slideshow

Machine Learning in Portfolio Management

Sonam Srivastava (@sonaam1234)

  • Crisp talk
  • Intermediate
  • Sun, 19 Aug