Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

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:

  1. Model management, including data cleaning, instrumentation and productionizing data science.
  2. Bad data and case studies of failure in building data products.
  3. Identifying and handling fraud + data security at scale
  4. Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
  5. Feature engineering and ML platforms.
  6. 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?

  1. Network with peers and practitioners from the data ecosystem
  2. Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
  3. Demo your ideas in the demo session
  4. 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

Anatta

Community Sponsors

Salesforce Ericsson freshworks
databricks

Exhibition Sponsors

Sapient Atlassian GO-JEK
Bayer

Bronze Sponsor

Sumologic Walmart Labs Atlan
Simpl Great Learning

Community Sponsors

Elastic Anaconda Aruba Networks

Hosted by

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

Suvrat Hiran

@suvrathiran

Fuzzy Deduplication of records at scale

Submitted Apr 15, 2019

Quality of the data stored have significant implications to a product/system that relies on information. Unfortunately, data is entred erroneously into the system creating duplicate entry. This leads to decrease in the quality of data retrieval for any product/system.
Particularly for Freshworks, we are looking at incorporating deduplication as a feature in our CRM product, Freshsales. Here deduplication would help customer’s sales teams organize their database more efficiently. Duplicates entry could be because of spelling mistakes, abbreviation usage, different order of words, etc. We have built a machine learning system which does deduplication for over fifty million records for streaming data as well as spark based system for static data.

Outline

  • Problem overview

  • Challenges

    • Scaling the solution to work for millions of records. Tackling n^2 problem (Each records if compared with every other record).
    • Model for duplicate detection should be robust to find duplicates even if there are spelling mistakes, phonetic matches, empty fields, punctuations, salutations, field mismatches, abbreviations, variations on phone numbers (state code, area code etc.)
    • Storing duplicate efficiently and keeping it cost effective.
    • Scoring and training model when there is lack of tagged data.
    • How to improve model based on user feedback.
  • Modeling methodology

    • Building training data wihout having clean tagged dataset.
    • Scaling deduplication using blocking. Records were grouped into blocks based on basic similarity.
    • Model choice and feature details
    • Model evaluation and metric
    • Active learning
  • Production deployment

    • Searching and storing duplicates for static and streaming data.
    • Spark was used to find duplicates already present in the system. Kafka consumer were written to find duplicates when any new record was added.
    • Spark and native python compatibility such that core model modules work for both.
    • Graph database for storage.
    • There are about ~100M records to be de-duplicated. We stored record ids as vertex and duplicate records were connected via edges. Graph holds close to ~40M vertex and and ~80M edges.
    • Fetching duplicates is O(1) operation.

Speaker bio

My name is Suvrat Hiran, I have been working with Freshworks for last 6 months. I work at juncture of engineering and data science. During my 7+ years of industrial experience I have built various machine learning products at scale. I have previously worked with adtech, marketing automation, enterprise AI companies helping them build machine learning products. I graduated from IIT Kharagpur in Statistics and Informatics in 2011.

Links

Slides

https://www.slideshare.net/secret/zCXQQj6qW9DSeD

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Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Hosted by

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