Ron Dagdag
Making neural networks portable with ONNX
#1about 6 minutes
Understanding ONNX as a portable format for ML models
Machine learning models are made portable across different frameworks and hardware using the ONNX open standard, similar to how PDF works for documents.
#2about 2 minutes
When to use ONNX for your machine learning projects
ONNX is ideal for deploying models across different programming languages, achieving low-latency inferencing, and running on resource-constrained edge or IoT devices.
#3about 12 minutes
Four methods for creating or acquiring ONNX models
Models can be obtained from the ONNX Model Zoo, built with tools like Azure Custom Vision, converted from existing frameworks like PyTorch, or used as an intermediary format.
#4about 7 minutes
Deploying models with the high-performance ONNX Runtime
The ONNX Runtime is a high-performance inference engine for deploying models to the cloud or edge devices, bridging the gap between data science and production software engineering.
#5about 4 minutes
Running an ONNX model in a Node.js application
A practical demonstration shows how to load an ONNX model and perform inference within a server-side Node.js application using the `onnxruntime-node` package.
#6about 9 minutes
Performing inference in the browser with ONNX Runtime Web
An emotion detection model is run directly in the browser using ONNX Runtime Web, showcasing client-side inference with JavaScript for privacy and offline capability.
#7about 3 minutes
Optimizing ONNX models for mobile and React Native
ONNX Runtime Mobile provides a lightweight solution for iOS and Android by converting models to a pre-optimized `.ort` format for smaller binary sizes.
#8about 8 minutes
Q&A on starting a career in machine learning
Advice is given on how software developers can enter the machine learning field by starting with model integration and deployment before diving deep into model creation.
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