The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
##Selection Process
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers.
##Travel grants
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
##Important Dates:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Krishnapriya Satagopan
@kpsatagopan
Submitted May 22, 2017
Data - There is a lot of it . But organizing it can be challenging, and analysis/consumption cannot begin until data is aggregated and massaged into compatible formats. These challenges grow more difficult as your dataset increases and as your needs approach the fabled “real time” status. Here, we’ll talk about how Python can be leveraged to collect data that is organized from many sources, standardized for analysis and consumption, and parallelized to scale with volume.
The topics covered will be Machine Learning, Pipelines and Monitoring. So, here we are going to look at an example of an ETL (Extract, Transform, Load) platform using Celery Pipelines and ELK Stack.
The talk begins with a brief of Machine Learning and the common problems faced. Then we progress further to explain how we tackled the machine learning problem using celery pipelines and monitoring strategies.
There will be a basic showcase from our ETL workflow and some dashboards to explain monitoring using the ELK stack (Elasticsearch, Logstash,Kibana) and Monit.
We will be learning and understanding the performances of the following tech stack.
Celery
RabbitMQ
Simple Queue Service (SQS)
Elastic Cache (Redis)
AWS technologies - Redshift, S3
ELK Stack (Elastic Search, Logstash, Kibana)
Krishnapriya (KP) is a hardcore Data Engineer with over 5 years of experience in the Data Engineering Space and the AWS stack. At Mad Street Den, she is part of the data science team and works closely with Data Scientists to build cost-effective cutting edge data products. She enables them to get their hands on all kinds of data sources in different forms and fidelities using scalable and robust data pipelines and workflows.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
Hosted by
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