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

Date

23 Nov 2019, Bangalore

Venue

NIMHANS Convention Centre, Bangalore

Call for proposals

Format:

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 anthillinside.editorial@hasgeek.com
  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 anthillinside.editorial@hasgeek.com

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: http://hsgk.in/2uvYKI9 http://hsgk.in/2ufhbWb http://hsgk.in/2vFVVJv http://hsgk.in/2vEF60T
  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: http://hsgk.in/2vDpag4 http://hsgk.in/2varOqt http://hsgk.in/2wyseXd 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: anthillinside.editorial@hasgeek.com
  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 support@hasgeek.com or call 7676332020. For queries on talk submissions, write to anthillinside.editorial@hasgeek.com

Propose a session

All proposals

Confirmed sessions

NLP bootcamp

Anuj Gupta (@anuj-gupta)

  • 1 upvotes
  • 0 comments
  • Wed, 15 May

Attention based sequence to sequence models for natural language processing

Madhu Gopinathan (@mg123)

  • 3 upvotes
  • 1 comments
  • Fri, 26 Apr

Unconfirmed proposals

Deploying Deep Learning models on the Edge (Android, IOS, ...)

A Naveen Kumar (@4nonymou5)

  • 1 upvotes
  • 0 comments
  • Fri, 19 Jul

Machine Learning Model and Dataset Versioning

Kurian Benoy (@kurianbenoy)

  • 1 upvotes
  • 0 comments
  • Thu, 18 Jul

Building a time series model using CNNs and GANs

prasenjeet acharjee (@pac1310)

  • 1 upvotes
  • 0 comments
  • Mon, 24 Jun

Design a real-time anomaly detection application using Spark and Machine Learning

ANKIT JAIN (@ankitjain22)

  • 1 upvotes
  • 1 comments
  • Sat, 15 Jun

iCASSTLE: Imbalanced Classification Algorithm for Semi Supervised Text Learning

Debanjana Banerjee (@debanjana)

  • 1 upvotes
  • 7 comments
  • Sat, 15 Jun

Generation of Newsletter using Natural Language Generation

Rajesh Gudikoti (@ragudiko)

  • 5 upvotes
  • 1 comments
  • Mon, 10 Jun

Greed based efficient reward disbursal

Ajeet Jha (@ajeetjha)

  • 1 upvotes
  • 6 comments
  • Tue, 21 May

Time Series Anomaly detection on structured data from IOT Network using CNN

prasenjeet acharjee (@pac1310)

  • 5 upvotes
  • 0 comments
  • Tue, 21 May

Network health predictions and optimization recommendation using Deep learning Neural network models and Reinforcement learning

Anuradha K (@anuradhak)

  • 15 upvotes
  • 15 comments
  • Tue, 7 May

Advanced NLP and Deep Learning for document classification - A case study in civil aviation safety prognosis

prabhakar srinivasan (@prabhacar7)

  • 1 upvotes
  • 3 comments
  • Wed, 1 May

Learning to Rank framework for product recommendation - Ranknet to LambdaMART to Groupwise scoring functions - experiments

narasimha m (@6544)

  • 2 upvotes
  • 4 comments
  • Wed, 1 May

Dataset Denoising : Improving Accuracy of NLP Classifier

Khaleeque Ansari (@khaleeque-ansari)

  • 2 upvotes
  • 3 comments
  • Tue, 30 Apr

Taking AI Products to Market

Puneeth N (@puneethnarayana)

  • 6 upvotes
  • 3 comments
  • Tue, 30 Apr

Practical Recommendation Systems: Scalability, Accuracy, Latency

Kunal Kishore (@kunalkishore)

  • 1 upvotes
  • 0 comments
  • Tue, 30 Apr

GAN-inspired Innovations in Computer Vision

Pushkar Pushp (@ppushp7)

  • 1 upvotes
  • 1 comments
  • Tue, 30 Apr

End to End Computer Vision paradigm with respect to advanced deep learning techniques.

Pushkar Pushp (@ppushp7)

  • 1 upvotes
  • 2 comments
  • Tue, 30 Apr

Large scale Machine Learning and data storage for CDP: transforming Digital Marketing

Kunal Kishore (@kunalkishore)

  • 1 upvotes
  • 1 comments
  • Tue, 30 Apr

Modeling the effects of blurriness in mobile ads

Abhijith C (@abhijith-c)

  • 1 upvotes
  • 1 comments
  • Tue, 30 Apr

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

Nilesh Patil (@nilesh-patil)

  • 1 upvotes
  • 1 comments
  • Tue, 30 Apr

Opening the Black Box: How to Interpret Machine Learning models; techniques, tools, and takeaways

Farhat Habib (@distantfedora)

  • 2 upvotes
  • 2 comments
  • Tue, 30 Apr

Recommendation @ Scale

Aditya Patel (@adityap)

  • 4 upvotes
  • 2 comments
  • Tue, 30 Apr

Using Locations for Online-Behaviour Prediction with Sparse Data

Nishant Oli (@nishantoli)

  • 1 upvotes
  • 2 comments
  • Tue, 30 Apr

Using AI for improving performance and design of ad creatives

Farhat Habib (@distantfedora)

  • 1 upvotes
  • 4 comments
  • Tue, 30 Apr

Tools for AI & ML for machine learning at Scale.

Saurabh Misra (@saurabh-appd)

  • 1 upvotes
  • 1 comments
  • Tue, 30 Apr

Feature selection and engineering using genetic algorithms and genetic programming

SIDHARTH KUMAR (@sidkumar)

  • 1 upvotes
  • 3 comments
  • Tue, 30 Apr

Explainable AI: Behind the Scenes

Manjunath (@manjunath-123)

  • 1 upvotes
  • 6 comments
  • Tue, 30 Apr

Interpret-ability as a bridge from Insights to Intuition in Machine and Deep Learning

Sai Sundarakrishna (@psgsai)

  • 2 upvotes
  • 1 comments
  • Mon, 29 Apr

Out of Distribution Detection in Deep Learning Classifiers

Akhil Lohia (@alohia)

  • 4 upvotes
  • 3 comments
  • Mon, 29 Apr

Learning to Rank recommendation - Ranknet to LambdaMART to Groupwise scoring functions - experiments, introduction to Tensorflow Ranking

narasimha m (@6544)

  • 3 upvotes
  • 4 comments
  • Mon, 29 Apr

Reinforcement Learning

Suraj Sheth (@shethsh)

  • 1 upvotes
  • 1 comments
  • Mon, 29 Apr

Introduction to Bayesian Networks(3 hour workshop)

usha rengaraju (@usharengaraju)

  • 1 upvotes
  • 5 comments
  • Sun, 28 Apr

Hands on Deep Learning for Computer Vision – Techniques for Image Segmentation.(6 hours workshop).

usha rengaraju (@usharengaraju)

  • 1 upvotes
  • 4 comments
  • Sun, 28 Apr

Tensorboard: Almost a one stop shop for Machine Learning Development

Tushar Pawar (@tuuushaar)

  • 1 upvotes
  • 5 comments
  • Sun, 28 Apr

Annotate This! A simple tool that seeks to simplify the creation of annotated text datasets - primarily for Hindi and other vernacular Languages

karmanya aggarwal (@calmdownkarm)

  • 1 upvotes
  • 3 comments
  • Sun, 28 Apr

Productionizing deep learning workflow with Hangar, <frameworkOfYourChoice> & RedisAI

Sherin Thomas (@hhsecond)

  • 1 upvotes
  • 4 comments
  • Fri, 26 Apr

Snorkeling in the deep: Bootstrapping an NLU model

Shubhangi Agrawal (@shubhangia)

  • 3 upvotes
  • 3 comments
  • Thu, 25 Apr

Non-Intent User Similarity for recommendation systems

Gunjan Sharma (@gunjan-sharma)

  • 2 upvotes
  • 1 comments
  • Thu, 25 Apr

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

Vanshit Malhotra (@vanshit)

  • 1 upvotes
  • 0 comments
  • 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)

Mithun Ghosh (@mithunghosh)

  • 1 upvotes
  • 2 comments
  • Wed, 17 Apr

A Guide on Dynamic Parameter Estimation for Causal Forecasting

Tanmoy Bhowmik (@tanmoyb)

  • 1 upvotes
  • 3 comments
  • Tue, 16 Apr

Accountable Behavioural Change Detection (VEDAR) using Machine Learning

Srinivasa Rao Aravilli (@aravilli)

  • 0 upvotes
  • 9 comments
  • Tue, 16 Apr

Use cases of Financial Data Science Techniques in retail

Sudipto Pal (@sudipto-pal)

  • 1 upvotes
  • 3 comments
  • Mon, 15 Apr

Email Data Analytics

Rahul Sharma (@rahulks)

  • 4 upvotes
  • 3 comments
  • Mon, 15 Apr

Building a Context-Aware Knowledge graph using Graph analysis & Language models

Shashank Rao (@shashankpr)

  • 28 upvotes
  • 1 comments
  • Sun, 14 Apr

Introduction to Probabilistic Programming - PyMC3 and Edward

Hariharan C (@harc)

  • 3 upvotes
  • 5 comments
  • Sat, 13 Apr

Hacking Self-attention architectures to address Unsupervised text tasks

Venkata Dikshit Pappu (@vdpappu)

  • 31 upvotes
  • 6 comments
  • Thu, 11 Apr

Industrialized Capsule Networks for Text Analytics

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • 1 upvotes
  • 8 comments
  • Wed, 3 Apr

Bandit algorithms to Reduce Cognitive Load on Customer Care Agents (Paper accepted for the demo track at SIGIR-2019)

Hrishi Ganu (@blah)

  • 1 upvotes
  • 6 comments
  • Tue, 26 Mar

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

Vikram Vij (@vikramvij)

  • 1 upvotes
  • 4 comments
  • Sun, 17 Mar

Essential Python Recipes for Deep Learning

Aakash N S (@aakashns)

  • 2 upvotes
  • 1 comments
  • Tue, 26 Feb

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

Tarang Shah (@tarang27)

  • 1 upvotes
  • 0 comments
  • Sat, 20 Oct

Virtual Assistant for High Volume Recruitment

Piyush Makhija (@piyushmakhija)

  • 1 upvotes
  • 0 comments
  • Mon, 15 Oct

Portfolio Optimization using Deep Reinforcement Learning

Sonam Srivastava (@sonaam1234)

  • 4 upvotes
  • 3 comments
  • Sun, 19 Aug