AI/ML to production with RedisAI

Sherin Thomas (~hhsecond)


Taking deep learning models to production and doing so reliably is one of the next frontiers of DevOps. 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

Workshop outline

  • Introduction to RedisAI
  • Installation and setup
  • Exporting PyTorch, Tensorflow, Spark, Scikit-Learn models using python redisai client
  • Saving exported binaries to RedisAI
  • Serving on GPU (yes you can serve traditional ML on GPU now)
  • High availability with sentinel
  • Setting up a cluster


  • The audience should have basic python knowledge and a brief understanding of deep learning.
  • A Laptop preferably running on Mac/Linux
  • Install pytorch from here
  • Install docker & redisai
  • pip install hangar
  • pip install redisai, the python client for redisai server

Content URLs:


Speaker Info:

I am working as a part of the development team of [Tensor]werk, 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 this includes RedisAI & hangar. 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 have contributed to the core of several widely used projects like PyTorch. I spend most of my research time on improving the interpretability of AI models using TuringNetwork. I have authored a deep learning book. I go by hhsecond on internet

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Id: 1138
Section: Data Science, Machine Learning and AI
Type: Workshop
Target Audience: Intermediate
Last Updated: