##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
Machine Learning in Production : Fundamentals and Updates
< Work in Progress >
When both technology and ecosystem are rapidly evolving, one of the prerequisites to excel is to focus on building things that either lasts longer or truely differentiates itself amongst currently available alternatives. If you are a Machine Learning practitioner, it’s not hard to end up in a situation where several research papers and prototypes of a new algorithms are out on standard datasets before even your older prototype makes it to production on real dataset. However, newer algorithms can become least of your concerns if fundamental plumbing, raw materials (data) and measurement metrics of system are set up correctly. This talk will focus primarily on setting up few key components of machine learning pipeline correctly such that you can efficiently and cost effectively move your prototypes to production.
< Work in progress >
Introduction [2 mins]
Clarify all Ws before starting (Why, Who, What) [5 mins]
See what is available already out there and ready to use. (Some latest updates in the ecosystem to set the stage for rest of the talk)[5 mins]
Garbage in garbage out (Let there be enough data! Data is expensive. How to optimise on training data generation?) [7 mins]
Modelling (Simple heuristics -- > simple model --> complex model, Launch and iterate, What’s the latest stuff?) [6 mins]
Integration and Deployment (In memory or micro service or lamda or on device?) [6 mins]
Conclusion [5 mins]
Machine Learning enthusiasts who wish to be good engineers :P
Krupal started his career as a Research Trainee at Hewlett-Packard Laboratories 6 years ago and currently leads all Machine Learning initiatives at Haptik. He is also recognised amongst less than 50 Google Developer Experts globally in Machine Learning for his open source contribution, technical blogs, public speaking and mentoring in the community. He specialises in rapid prototyping of machine learning algorithms and has efficiently deployed multiple models to production addressing different use-cases. He likes to mentor engineers and researchers to help them align their efforts in result-oriented direction. A part from technology, he also likes to read and learn about product development and business strategy. One of his dreams is to solve a real world problem which positively impacts atleast one of the fundamental needs of human race.