Anthill Inside Miniconf – Pune
Machine Learning, Deep Learning and Artificial Intelligence: concepts, applications and tools.
Nov 2017
20 Mon
21 Tue
22 Wed
23 Thu
24 Fri 10:00 AM – 05:50 PM IST
25 Sat
26 Sun
Swapnil Dubey
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)
- AWS
- 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).
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