PyData NYC 2022

Most production information retrieval systems are built on top of Lucene which use tf-idf and BM25. Current state of the art techniques utilize embeddings for retrieval. This workshop will cover common information retrieval concepts, what companies used in the past, and how new systems use embeddings.

Outline:
- Overview of search retrieval
- Non deep learning based retrieval
- Embeddings and Vector Similarity Overview
- Serving Vector Similarity using Approximate Nearest Neighbors (ANN)

By the end of the session, a participant will be able to build a production information retrieval system leveraging Embeddings and Vector Similarity using ANN. This will allow participants to utilize state of the art technologies / techniques on top of the traditional information retrieval systems.

This talk is for a data scientist or ML engineer looking to serve their Pytorch models in production.

It will cover post training steps that should be taken to optimize the model such as quantization and JIT.
It will also walk the user in packaging and serving the model through Facebook’s TorchServe.