Challenges and approaches for instrumenting and cleaning 'real'/ ugly data

Jul 2019

22 Mon

23 Tue

24 Wed

25 Thu 09:15 AM – 05:45 PM IST

26 Fri 09:20 AM – 05:30 PM IST

27 Sat

28 Sun

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Tickets

Pinned update

The Fifth Elephant Winter edition starts at 9:30 am; live stream for members This update is for participants only

##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##Highlights:

1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.

##Why you should attend?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

##Contact details:
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

#Sponsors:

Sponsorship Deck.
Email sales@hasgeek.com for bulk ticket purchases, and sponsoring 2019 edition of JSFoo:VueDay.

JSFoo:VueDay 2019 sponsors:

#Platinum Sponsor

#Community Sponsors

#Exhibition Sponsors

#Bronze Sponsor

#Community Sponsors

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The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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This submission has been added to the schedule

Challenges and approaches for instrumenting and cleaning 'real'/ ugly data

Submitted Jul 22, 2019

Session type: Birds of a Feather session of 1 hour

Most practicing data scientists have those “bad data days” where you realize the data is corrupt, or not what you assumed the data to be, or labels are not right or even worse. What if we work in a paradigm assuming: “all data is corrupt, some is useful”, while at the same time instrumenting for any data which can be captured? In such a setting, how to go about various day-to-day data cleaning challenges, prioratizing data collection, building tooling for data quality etc?

##Who should attend this BOF?

Anyone who is eager to share stories around collecting richer data or making data cleaner in a systematic way
Data geeks
If you want to learn how to handle data more carefully and with respect

Outline

In this session we will share our experience on multiple things associated with collecting/instrumenting data and converting it to useful/accessible form. Specifically, the focus will be on emerging data in space of agriculture, biology, telemetry, images. Few of the topics which would be discussed are:

Dealing with incomplete data
Data accuracy issues
Tools for data dictionary
Process of data cleaning
QA for right data instrumentation

##We will cover the following topics in this session:

Data basics like meta data store, data dictionary, documentation of data flow -- challenges and tooling.
Types of data issues including discovery of known unknowns: incomplete data, improper instrumentation, corrupted pipeline, varying quality, quality of labels -- what are the reasons?
Categorising sources and types of issues -- ways of dealing with each type.
Steps/milestones in journey for better data: legacy data, new data
How to know what to instrument -- various approaches.
Suggestions and recommendations to approach the problem.

Requirements

You should have played with data and felt the pain of something being off. :)

Speaker bio

Kranthi Mitra -- Principal Data Scientist at Swiggy
Raghotham Sripadraj -- Senior Data Scientist at Ericsson
Elvis Joel D’Souza -- Director of Product Engineering at Sensara
Karnam Vasudeva Rao -- Senior Scientist in the data science team at Bayer

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