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:
Story of Building a Telecom Data Analytics Solution
Telecom data is quite complex - consisting of hundreds of continuous and categorical variables that capture the details of millions of users consisting of plans, services, roaming, phone/SMS usage, revenue, and, cost, etc. Through interactions with customer leadership, we arrived on the business objective of our solution as optimizing the existing plans and services and maximizing the profit. We used statistics and ML-based feature selection techniques and combined with domain knowledge to recommend the features to the Subject Matter Experts (SMEs). Using our solution the SMEs were able to identify the end-user plans that were not profitable and able to come up with strategies to maximize the profits, tailor existing services and offer new services. In this crisp talk, I will share the details on how data science played a role in assisting the telecom customer to derive valuable insights.
In this talk, I will share my knowledge and experience in building a data science solution for telecom domain.
Here is an outline of my talk:
1. Define business objectives and translating the customer requirement into a data science problem for telecom domain.
2. Explain the data - Telecom end-user data contains thousands of attributes. What are the attributes required for the business goals? How do we use domain knowledge and data science to do feature selection?
3. What’s the data processing pipeline? How to keep telecom SMEs involved in feature selection and identifying the opportunities to maximize the profits?
4. Details of categorical feature selection techniques and the ML model that assisted the telecom SMEs.
5. The takeaway message from Telecom data analytics.
PhD in Applied Mathematics with multiple years of work experience in data science and ML. Currently working as a Data Scientist at SAP Labs. Prior to SAP, worked at Shell India ML group as a data scientist.