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.
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
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email firstname.lastname@example.org
JSFoo:VueDay 2019 sponsors:
Ghostbusters: Optimizing debt collections with survival models
Session type: Full talk of 40 mins
A pay-later solution like Simpl comes with risk - some customers don’t pay their bill on time. When this happens, our collections team calls them up and gently reminds them that their bill is due. Some people even try to vanish - they ghost us - without paying their bill, resulting in escalation to our skip trace team.
In this talk I’ll go over how we use survival models to optimize our calling team by deciding who has skipped (and needs a trace), who should get a gentle reminder, and in what order of priority.
This talk is about using survival models to optimize the process of making collection calls (“dear sir, please pay your bill, it’s overdue”).
- An overview of how the calling process is structured. This will give an understanding of what we’re trying to optimize.
- Discuss why moving people from one level of the process to another automatically and optimally is important for recovering money.
- Get an understanding of why data-backed decisions are important for overall efficiency. Is it worth it to make 7 calls per user or should you escalate after 4 calls?
- Understand how using panel data for user behavior is significantly different from more standard classifiers which use cross-sectional data.
A brief introduction to survival models:
- What survival models are, and where they are traditionally used. Get an introduction to basic terminology like survival function, hazard rate, censoring, etc.
- Take a look at non-traditional applications of survival models in fields like sales lead prioritization, marketing automation, etc.
How we use survival models:
- How math concepts are directly relevant to the business - a hazard function is directly useful as a lead score, while a survival function tells us who the ghosts are. Math => business decisions.
- Constructing hazard curves via parametric (Weibull) and non-parametric (Kaplan-Meier) and connecting them to our real data.
- Cox proportional model
- Data limitations force us to use censored models.
- Take a look at productionizing these models; how to use this information to make better decisions. One model can solve many problems (escalation, lead scoring, write-off, etc.)
This talk is accessible to those with some prior experience in statistics and/or machine learning
Fasih is a data scientist at Simpl, India’s top pay later platform. When he’s not busy playing video games, he’s busy writing about all-things-Bayes and functional programming. Prefers adrak-wali-chai over coffee, suggests ordering from Tata Cha over Chai Point, and paying using Simpl.
- Implementing TSum: An Algorithm for Table Summarization - http://fasihkhatib.com/2018/10/21/Implementing-TSum-An-Algorithm-for-Table-Summarization
- Frequentism vs Bayesianism - http://fasihkhatib.com/2019/05/10/The-Machine-Learning-Notebook-Frequentism-vs-Bayesianism/