Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

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:

  1. Model management, including data cleaning, instrumentation and productionizing data science.
  2. Bad data and case studies of failure in building data products.
  3. Identifying and handling fraud + data security at scale
  4. Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
  5. Feature engineering and ML platforms.
  6. What it takes to create data-driven cultures in organizations of different scales.

Highlights:

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?

  1. Network with peers and practitioners from the data ecosystem
  2. Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
  3. Demo your ideas in the demo session
  4. 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

Contact details:

For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

Sponsors:

Sponsorship Deck.
Email sales@hasgeek.com for bulk ticket purchases, and sponsoring 2019 edition of JSFoo:VueDay.

JSFoo:VueDay 2019 sponsors:

Platinum Sponsor

Anatta

Community Sponsors

Salesforce Ericsson freshworks
databricks

Exhibition Sponsors

Sapient Atlassian GO-JEK
Bayer

Bronze Sponsor

Sumologic Walmart Labs Atlan
Simpl Great Learning

Community Sponsors

Elastic Anaconda Aruba Networks

Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Sherin Thomas

@hhsecond

Tutorial: Taking deep learning to production with RedisAI

Submitted May 30, 2019

Taking deep learning models to production, and doing so reliably, is one of the next frontiers of DevOps. This talk introduces RedisAI, a joint effort by [tensor]werk and RedisLabs. RedisAI is a Redis module that adds tensors & graphs as Redis data types, enabling execution of deep learning graphs on the CPU and GPU using multiple backends (PyTorch, TensorFlow, and ONNXRuntime) simultaneously, while exposing a full tensor API for scripting. In this talk, we will demonstrate deploying a deep learning model to production in a highly available environment and lay down the roadmap towards 1.0.

Outline

Year 2018 was the year of model servers. There were numeroius initiatives for building a reliable, interoperable deep learning deployment toolkits but so far we don’t have an easy tool that can reliably handle the deep learning models from all the frameworks. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. In this talk we will introduce RedisAI, a joint effort by [tensor]werk and RedisLabs that introduces tensors and graphs as new Redis data types and allows to execute graphs over tensors using multiple backends (PyTorch, TensorFlow, and ONNXRuntime), both on the CPU and GPU. The module also supports scripting with TorchScript, which provides a Python-like tensor language that can be used to facilitate pre- and post-processing operations, like input shaping or output ensembling. In addition, thanks to its support for the ONNX standard, including ONNX-ML, RedisAI is not strictly limited to deep learning, but it offers support for general machine learning algorithms. In this talk, we will demonstrate a full journey from training a model to deploying to production in a highly available environment. Last, we will lay down the roadmap for the future, like automated batching, sharding, integration with Redis data types (e.g. streams) and advanced monitoring. The talk will include sample code, best practices and a live demo.

Who should attend this tutorial:

  • Leads who are managing a DL, ML, and traditional ML teams
  • DL/ML engineers
  • DL/ML researchers who needs to to interact with the engineering team for production deployment
  • DevOps
  • If you plan to do production deployment of DL/ML/traditional ML products

Requirements

Participants attending this tutorial must install the following software before attending the session:

  1. Laptops with Linux or Mac OS installed. If you have a Windows machine, set up a cloud instance.
  2. Docker
  3. Python 3.6+
  4. pip install PIL
  5. pip install Numpy
  6. pip install redisai==0.3.0
  7. pip install mlut[all]
  8. Jupyter notebook

Speaker bio

I am working as a part of the development team of tensorwerk, an infrastructure development company focusing on deep learning deployment problems. I and my team focus on building open source tools for setting up a seamless deep learning workflow. I have been programming since 2012 and started using python since 2014 and moved to deep learning in 2015. I am an open source enthusiast and I spend most of my research time on improving interpretability of AI models using TuringNetwork. I am part of the core development team of Hangar and RedisAI and a constant contributor to PyTorch source. I also have authored a deep learning book. I go by hhsecond on internet

Links

Slides

https://docs.google.com/presentation/d/1R_HrFWayzKEpom24zVfSF0VGbOfBgLyR0XTl1hNxT9E/edit?usp=sharing

Comments

{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more