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 email@example.com
JSFoo:VueDay 2019 sponsors:
Birds of a Feather: ML in production
Session type: Birds of a Feather session of 1 hour Session type: BOF session of 1 hour
Most ML effort stagnates at the stage of building ad-hoc models, with only thin layers of customization around them. This is mostly okay, but there are usually no guarantees about elasticity, uptime or even accuracy (since updating models is non-trivial) - all of which are crucial to business. This BoF invites the audience to discuss problems, paradigms and best practices around deploying machine learning to production.
ML In Production - Early Stages vs At Scale
Usually the easiest way to deploy models is via exposing serialized versions of them to applications through HTTP. This does not scale well for various reasons. APIs may become more complex as applications demand ML functionality beyond just making predictions. As models grow in size, they may become more expensive to deploy and host. As web traffic grows, models must become more and more performant not only in predicting results but also in terms of their availability. Moreover, not all models can be easily deployed over a REST API in the first place. In many cases there are multiple models and multiple endpoints - which eventually become difficult to manage.
ML as a Service vs ML as a Product
As ML gets more commoditized, organizations find themselves inevitably drawn into the service vs product debate. MLaaS solutions are highly modular and customizable, but place a knowledge overhead on practitioners. On the other hand, ML products are well defined, making them easier to build in an Agile sense, with a relatively lower overhead on knowledge and skills. But they can be too rigid and monolithic. The skills and efforts that go into these two paradigms can be widely different, causing teams and organizations to rethink their approaches often.
ML on Cloud vs ML on Premise
The abstractions around primitive ML infrastructure provided by cloud services and the self-contained production environments required by on-premise solutions - both have a significant learning curve. Both paradigms place different, but equally demanding constraints on data scientists and application developers. Cloud vs on-premise may not always be a choice, but when organizations have to prioritize what kind of expertise they want to develop, this becomes relevant.
Data Scientists, Application Developers and SysAdmins
This is more of a cultural than a technical issue. Going from a jupyter notebook to a packaged, deployable solution is messy, at best. We do see data scientists working more often with developers and sysadmins than before - the boundaries of job descriptions are thinning - but this is not as frictionless as we’d like it to be.
- Jaidev Deshpande
- Sherin Thomas
- Simrat Hanspal