The Open Neural Network Exchange (ONNX) standard has emerged for representing deep learning models in a standardized format. In this talk, I will discuss:
1. ONNX for exporting deep learning computation graphs, the ONNX-ML component of the specification for exporting both traditional ML models, common feature extraction, data transformation and post-processing steps.
2. How to use ONNX and the growing ecosystem of exporter libraries for common frameworks (including TensorFlow, PyTorch, Keras, scikit-learn and Apache SparkML) to deploy complete deep learning pipelines.
3. Best practices for working with and combining these disparate exporter toolkits, as well as highlight the gaps, issues, and missing pieces to be taken into account and still to be addressed.
Data scientists & researchers – latest & greatest
Production – stability, control, minimize changes, performance
Business – metrics, business impact, product must always work!
Separation of concerns
Portability of models
Ability to build a single, optimized stack of components for:
Model execution
Model analysis, visualization
Monitoring