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:
Similarity Search for Product Matching @ Semantics3
Session type: Lecture Session type: Full talk of 40 mins
One of the major offerings of Semantics3 is our universal product data catalog gathered through large scale indexing of the public web. For each catalog, duplicated entries of the same product across multiple retailers need to be merged/removed. In this talk, we will go through the technical challenges in such a large scale “product matching” system, where millions of products are often compared against millions of others (leading to trillions of pair-wise comparisons). Both traditional and state-of-the-art approaches will be discussed in solving this task.
Introduction [~ 5 mins]
This section will present an overview of the problem, the use cases that motivate it and establish the tone for the rest of the presentation.
- Product Matching: What is it and Why is it important?
- Similarity Search for Product Matching: What is it and how does it speed up matching?
- Example Case for Similarity Search: Sample product document and sample query document to explain the following sections.
Traditional Text Search Approaches [~ 5 mins]
This section will cover our intial attempt at the similarity search problem using traditional text based methods largely leveraging elasticsearch.
- Overview of how we set up the problem
- Bottlenecks we hit and available tuning options
- Examples of real queries
Lessons from Traditional Text Search Approaches [~ 5 mins]
This section will cover some of the key insights we gleaned from traditional text approaches and how we needed to reframe the problem.
- The nature of our data/problem and why elasticsearch wasn’t a good fit.
- Need for indexing multi-modal data
- Examples of failed cases
- Search is only as good as the document’s representation.
Representation Learning [~ 10 mins]
This section would cover how we reframed this as a representation learning problem and the different network architectures we tried, how we suited it to our needs, what worked/didn’t work and the challenges we faced along the way.
- How we reframed the problem
- Different network architectures we tried and their results.
- Examples of success cases which had failed previously.
- Infrastructure and scaling challenges
Infrastructure Challenges [~ 5 mins]
Solving the representation problem didn’t necessarily solve the similarity search problem. We only had a way to sufficiently represent all the product information on the vector space. This section will cover the infrastructure challenges, the options we considered and how we ended up choosing FAISS.
- Challenges, Constraints
- Re-evaluating Elasticsearch
- Evaluating FAISS
- Key bencmarks
Conlusion [~ 2 mins]
Familiarity with text search paradigms will be a good-to-have (not essential).
Abishek is a member of the data science team at Semantics3, which offers data and AI solutions for ecommerce marketplaces (catalog generation & enrichment, seller on-boarding) and logistics companies (HTS/tariff classification, attribute enrichment). Among these, Abishek is the lead data scientist working on product matching and catalog generation.
- FAISS: https://github.com/facebookresearch/faiss
- Billion Scale Similarity Search: https://arxiv.org/pdf/1702.08734.pdf
- Talk on Product Matching at Fifth Elephant 2017: https://www.youtube.com/watch?v=6XaGsDqT_m4
- LinkedIn: https://www.linkedin.com/in/abishek-bhat-a5985616/
- GitHub: http://github.com/abishekk92
- Blog: https://www.semantics3.com/blog/author/abishek/