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:
It's Launched! Why do I need to continuously benchmark and monitor my computer vision model?
Session type: Short talk of 20 mins
Open source models like Imagenet and Resnet have opened the door to enable millions of computer vision use cases. But launching enterprise computer vision application doesn’t end when the model is trained - that’s just the first step. To build an end-to-end solution, one needs to understand the appropriate steps and best practices to follow.
If you are planning to build and launch a computer vision application, you need to consider what happens to the ML model after it has reached a level of accuracy and performance for your use case. How exactly are you going to architect your application software? How are you going to deploy and scale models to potentially hundreds or thousands of devices in production? How do you extract useful information from the models for retraining? Where do you store the results and metrics of the predictions? Is your application mission critical, or can the model be run offline? How do you setup a vision ML pipeline that doesn’t break the bank or require an army of engineers and computer vision experts to maintain?
In this presentation, Dori will focus specifically on challenges that every enterprise application developer will face when building a computer vision application and how to set the enterprise up for success. The talk will draw from previous examples of how typical software pipelines have been set up and demonstrate the best practices to quickly build machine learning computer vision pipelines that can scale to millions of deployments. The talk will also cover best practices of how to effectively benchmark and monitor your machine learning model in order to continuously improve the quality and system performance.
Evolution of Computer Vision
- Introduction into how computer vision models have evolved
- Accuracy & performance improvements over the past few years
- Acceptability of deep learning in enterprise use cases
- Discussion of the opportunity that Open Source has created for deep learning
- The disparity of choice that open source has created
The Enterprise Challenge
- Why do enterprises still struggle to productize a single model?
- What questions need to be answered to create an AI application?
- What tools & infrastructure are needed?
Setting Up a Robust Pipeline
- A formula for success to set up a robust ML development pipeline
- Why benchmarking and monitoring are key steps of the ML development lifecycle
- Model/System metrics vs Business Metrics - what is important?
- How to extract value and useful data from a model in production
Come with an open mind to learn.
Nitin Gupta helped found Dori with the vision of enabling the applied AI market with a platform that accelerates the adoption of AI/ML across major vertical industries. He has 12+ years of experience leading product/architecture in complex edge, mobile, and computer vision systems.
Prior to Dori, Nitin was a product lead at Google responsible for commercializing AI/ML systems for VRCore/ARCore products within the Daydream team and also led teams at Pebble and Qualcomm. His Ph.D. work involved novel search algorithms to achieve timing closure for embedded SOC designs.
- Speaker Profile: https://www.linkedin.com/in/nitin-gupta-a37b0289/
- Dori Overview: https://www.youtube.com/watch?v=AIZ0QqNH_0c