by The Fifth Elephant

The Fifth Elephant winter edition 2019

Winter edition of India's most renowned conference on big data and data science

The Fifth Elephant winter edition 2019

The Fifth Elephant winter edition 2019

Winter edition of India's most renowned conference on big data and data science

by The Fifth Elephant
date_range

Date

18 Jan 2019, ISDI ACE, Lower Parel, Mumbai

place

Venue

ISDI ACE

About

The Fifth Elephant is rated as India’s best conference on big data, data science and application of data to real-life use cases.

It is a conference for practitioners, by practitioners. The Fifth Elephant completed its seventh edition in Bangalore, on 26 and 27 July 2018. The Bangalore edition caters to data and ML engineers, architects, technologists, data scientists, product managers, researchers and business decision-makers.

Talks

  • Data pipeline on day one of your startup: cost and scale sensitive!

    Kumar Puspesh, CTO and co-founder at Moonfrog

    Data pipeline on day one of your startup: cost and scale sensitive!

    Rough outline

    1. Business Requirements
    2. Usecase
      • Having a scalable system for data ingestion
      • Data design - Specific or Generic?
      • Querying interface - why stick to SQL?
      • Query interface users - skills, requirements and expectations
    3. Data ingestion
      • High throughput stats service
      • Thin client: Badger
      • High throughput Ingestion backend
      • Hot loading to Redshift
    4. Data Warehousing
      • Data design in Redshift and data lake
      • Tuning for scale
      • Taking care of Querying patterns of Product Managers and Data scientists
    5. S3 as Data Lake
      • On demand Data loading and querying: OnDemand Table(s)
        • Gotchas
      • Flexibility for complicated analysis: Adhoc redshift cluster(s)
        • Gotchas
    6. Scaling up
      • Typical bottlenecks and solutions we tried
    7. Learnings
  • Reducing cost of production AI: a feature engineering case study

    Venkata Pingali, CEO and co-founder at Scribble Data

    Reducing cost of production AI: a feature engineering case study

    1. Feature Engineering Overview
    2. Typical Feature Engineering Cycle
      • Trends
    3. Detailed Cost Drivers
      • Examples: Reconciliation & auditing, change management
    4. Indicative Quantitative Improvement
    5. Detailed discussion of each driver
  • Data governance: lessons on data usage and data controls from finance domain

    by Kaushik Bhatt, Vice President at Wells Fargo

    Data governance: lessons on data usage and data controls from finance domain

    Data Governance session outline will cover,
    - systematic approach to identifying enterprise data assets, who owns them and who can access them - data protection approach - data catalogue, data profiling and data quality

  • Detecting anomalies within Flipkart's fulfillment network

    Govind Pandey, Senior Engineering Manager at Flipkart

    Detecting anomalies within Flipkart's fulfillment network

    <Work in progress>

    Motivation
    Approach
    Learnings

  • Role of data in solving capacity and efficiency problems in real-time logistics

    by Piyush Srivastava, Director of Engineering for Delivery Team at Swiggy

    Role of data in solving capacity and efficiency problems in real-time logistics

    1. Introduction and Context
    2. The Capacity Problem - what is it; why it is important?
    3. The Efficiency Problem - what, why and the necessary trade-offs
    4. Data and its Nature
    5. Challenges with Accurate Data Capture
    6. Challenges with high Variance
    7. Real-time Vs. historical data
    8. Representing Capacity
    9. Aggregated capacity (Zone-level)
    10. Point-in-time-capacity (Order-level)
    11. Journey and Results: Solving for Capacity
    12. Efficiency Levers
    13. Predictions and accounting for errors
    14. Trade-offs
    15. Optimal Assignment
    16. Batching
    17. Aggregate Analysis Vs. Specific Analysis
    18. Pitfalls of Aggregate Analysis
    19. Conclusions
  • Patterns for building a scalable Data Platform

    Jayesh Sidhwani, Data Infrastructure Team Lead at Hotstar

    Patterns for building a scalable Data Platform

    • Ingestion Patterns
      • Unified Ingestion Proxy
      • Schema Definitions
      • In-flight enrichments
      • Highly Available
    • Storage Patterns
      • Decouple storage and compute
      • Query Lineage & Optimization
      • Noisy Neighbour
    • Consumption Patterns
      • Single GUI and a programmatic interface. All the magic underneath
      • Parity between streaming and stationary data
  • Advancing Data Science for Financial Inclusion: Trusting Social's Journey

    by Thuong Nguyen, Research Scientist at Trusting Social

    Advancing Data Science for Financial Inclusion: Trusting Social's Journey

    • Introduction to credit scoring
    • Limitation of credit scoring
    • Alternative credit scoring
    • Data Science challenges in alternative credit scoring
    • How Trusting Social works?

Venue

ISDI ACE
One Indiabulls Centre, 7th Floor
Tower 2A, One, Tulsi Pipe Rd, Saidham Nagar, Lower Parel
Mumbai 400012
Maharashtra
IN

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