Deploying End-to-End Deep Learning Pipelines with ONNX
Who is this presentation for?ML engineers, production engineers, data scientists
A deep learning model is often viewed as fully self-contained, freeing practitioners from the burden of data processing and feature engineering. However, in most real-world applications of AI, these models have similarly complex requirements for data pre-processing, feature extraction and transformation as more traditional ML models.
Any non-trivial use case requires care to ensure no model skew exists between the training-time data pipeline and the inference-time data pipeline. This is not simply theoretical – small differences or errors can be difficult to detect but can have dramatic impact on the performance and efficacy of the deployed solution.
Despite this, there are currently few widely accepted, standard solutions for enabling simple deployment of end-to-end deep learning pipelines to production. Recently, ONNX has emerged for representing deep learning models in a standardized format. While this is useful for representing the core model inference phase, we need to go further to encompass deployment of the end-to-end pipeline.
In this talk I will introduce ONNX for exporting deep learning computation graphs and the ONNX-ML extension of the core specification, for exporting both “traditional” ML models as well as common feature extraction, data transformation and post-processing steps. I will cover how to use ONNX and the growing ecosystem of exporter libraries for common frameworks (including TensorFlow, PyTorch, Keras, scikit-learn and others) to deploy complete deep learning pipelines, as well as the gaps and missing pieces to be taken into account and still to be addressed.
Prerequisite knowledgeBasic knowledge of deep learning & related frameworks would be useful.
What you'll learn
Nick Pentreath is a principal engineer in IBM’s Center for Open Source Data & AI Technologies (CODAIT), where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations. He has also worked at Goldman Sachs, Cognitive Match, and Mxit. He is a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.
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