##About the event
When it comes to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI), three aspects are crucial:
- Clarity of fundamental concepts.
- Insights and nuances when applying concepts to solve real-world problems.
- Knowledge of tools for automating ML and DL.
Anthill Inside Miniconf will provide understanding on each of these fronts.
This miniconf is a full day event consisting of:
- 3-4 talks each, on concepts, applications and tools.
- Birds of Feather (BOF) sessions on focussed topics.
We are accepting proposals for:
- 10 to 40-minute talks, explaining fundamnetal concepts in math, statistics and data science.
- 20 to 40-minute talks on case studies and lessons learned when applyng ML, DL and AI concepts in different domains / to solve diverse data-related problems.
- 10 to 20-minute talks on tools on ML and DL.
- Birds of a Feather (BOF) sessions on failure stories in ML, to what problems / use cases should you use ML and DL, chatbots.
- 3-6 hour hands-on workshops on concepts and tools.
Hands-on workshops for 30-40 participants on 25 November will help in internalizing concepts, and practical aspects of working with tools.
Workshops will be announced shortly. Workshop tickets have to be purchased separately.
##Target audience, and why you should attend this event
- ML engineers who want to learn about concepts in maths, stats and strengthen foundations.
- ML engineers wanting to learn from experiences and insights of others.
- Senior architects and decision-makers who want to quick run-through of concepts, implementation case studies, and overview of tools.
- Masters and doctoral candidates who want to bridge the gap between academia and practice.
Proposals will be shortlisted and reviewed by an editorial team consisting of practitioners from the community. Make sure your abstract contains the following information:
- Key insights you will present, or takeaways for the audience.
- Overall flow of the content.
You must submit links to videos of talks you have delivered in the past, or record and upload a two-min self-recorded video explaining what your talk is about, and why is it relevant for this event.
Also consider submitting links to the following along with your proposal:
- A detailed outline, or
- Mindmap, explaining the structure of the talk, or
- Draft slides.
##Honorarium for selected speakers; travel grants
Selected speakers and workshop instructors will receive an honorarium of Rs. 3,000 each, at the end of their talk. We do not provide free passes for speakers’ colleagues and spouses.
Travel grants are available for 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, mention this in the field where you add your location. Anthill Inside Miniconf is funded through ticket purchases and sponsorships; travel grant budgets vary.
Anthill Inside Miniconf – 24 November, 2017.
Hands-on workshops – 25 November, 2017.
For more information about speaking, Anthill Inside, sponsorships, tickets, or any other information contact email@example.com or call 7676332020.
Doing Data Science on Cloud
With the increase in data size for running DS models,it is important to look into possible infrastructure options which provide enough scalability to run DS algo successfully.Optimal use of infrastructure in terms of cost is the need of hour.For example,running task using multiple GPU for finite amount of time. Almost all the Cloud vendors(AWS,Google,Microsoft) provide different kind of services for this situation.This talk will primarily make a comparison into advantages and disadvantes of such services provided by cloud providers.It will also look into various options for running tasks in a particular cloud provider.
The presentation is purely on the basis of study which we did in the past.
Data scince on Cloud:
Importance of running DS on Cloud?
Options for running ML on cloud platform
- Using native compute and storage only.
- Hosted Data platfrom
- Machine Learning Services
- Congnitive API Services.
Demo : Using Cognitive Services of Google Cloud Platform- GCP vision API.
Options for running scalable DS models on Cloud:(Advantage, Disadvantage, Pricing)
- Azure Machine learning
- Google Cloud ML
Other providers: IBM bluemix vs Sense.io vs Domino datalab vs Datajoy
Demo: Running DS models using Tensorflow on Google Cloud ML(Using GPUs).
- Swapnil is right now contributing to Schlumberger Data Science team applying analytics in field of Oil and Natural Gas.Prior to this he was part of Snapdeal Realtime Analytics team as Lead Enginner.
Swapnil in the past has worked as Cloudera Trainer.He belives in learning and sharing his learning across the community.A frequent speaker in meetups and active presenter in conferences.
With more than 8+ years of experience, Swapnil has contributed in Domains of BFSI,Ad Serving and eCommerce with Hadoop,Spark and GCP as primary tech stack.
Past conferences & Meetups:
- Dr Dobbs conference-Bangalore- April 11-12,2014
- Ekansh Verma is right now working with Schlumberger Data Scince team as Data scientist.He has done his Bachelors, Biomedical Engineering from IIT Chennai.He has good understanding of Deep Learning concepts. His primary expertise lies in Image classfication.