Ron Dagdag

Making neural networks portable with ONNX

What if your ML models were as portable as a PDF? Learn how ONNX creates a universal standard to run any model on any hardware, even in the browser.

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.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

The shift to on-device AI models in smartphones
00:48 MIN

The shift to on-device AI models in smartphones

Fake or News: Coding on a Phone, Emotional Support Toasters, ChatGPT Weddings and more - Anselm Hannemann

Building an open source community around AI models
04:28 MIN

Building an open source community around AI models

AI in the Open and in Browsers - Tarek Ziadé

The hardware requirements for running LLMs locally
03:55 MIN

The hardware requirements for running LLMs locally

AI in the Open and in Browsers - Tarek Ziadé

Using Chrome's built-in AI for on-device features
06:44 MIN

Using Chrome's built-in AI for on-device features

Devs vs. Marketers, COBOL and Copilot, Make Live Coding Easy and more - The Best of LIVE 2025 - Part 3

Using AI to overcome challenges in systems programming
02:49 MIN

Using AI to overcome challenges in systems programming

AI in the Open and in Browsers - Tarek Ziadé

Technical challenges of shipping a cross-platform browser
09:38 MIN

Technical challenges of shipping a cross-platform browser

Developer Time Is Valuable - Use the Right Tools - Kilian Valkhof

The evolving role of the machine learning engineer
02:20 MIN

The evolving role of the machine learning engineer

AI in the Open and in Browsers - Tarek Ziadé

Integrating AI into Firefox while respecting user privacy
08:40 MIN

Integrating AI into Firefox while respecting user privacy

AI in the Open and in Browsers - Tarek Ziadé

Featured Partners

Related Videos

See all videos
Privacy-first in-browser Generative AI web apps: offline-ready, future-proof, standards-based30:13

Privacy-first in-browser Generative AI web apps: offline-ready, future-proof, standards-based

Maxim Salnikov

Generative AI power on the web: making web apps smarter with WebGPU and WebNN32:27

Generative AI power on the web: making web apps smarter with WebGPU and WebNN

Christian Liebel

Prompt API & WebNN: The AI Revolution Right in Your Browser30:36

Prompt API & WebNN: The AI Revolution Right in Your Browser

Christian Liebel

From ML to LLM: On-device AI in the Browser27:23

From ML to LLM: On-device AI in the Browser

Nico Martin

Trends, Challenges and Best Practices for AI at the Edge29:50

Trends, Challenges and Best Practices for AI at the Edge

Ekaterina Sirazitdinova

How AI Models Get Smarter35:16

How AI Models Get Smarter

Ankit Patel

Your Next AI Needs 10,000 GPUs. Now What?29:52

Your Next AI Needs 10,000 GPUs. Now What?

Anshul Jindal & Martin Piercy

WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA22:07

WWC24 - Ankit Patel - Unlocking the Future Breakthrough Application Performance and Capabilities with NVIDIA

Ankit Patel

Related Articles

View all articles
CH
Chris Heilmann
With AIs wide open - WeAreDevelopers at All Things Open 2025
Last week our VP of Developer Relations, Chris Heilmann, flew to Raleigh, North Carolina to present at All Things Open . An excellent event he had spoken at a few times in the past and this being the “Lucky 13” edition, he didn’t hesitate to come and...
With AIs wide open - WeAreDevelopers at All Things Open 2025
DC
Daniel Cranney
Dev Digest 198: 30 years of JS, In-Browser AI, How Attackers Abuse GenAI
Inside last week’s Dev Digest 198 . 🎂 30 years of JavaScript ⏰ How long is a JavaScript second 💻 Clean code in Angular 🤦‍♂️ AI makes different mistakes than humans 👨‍💻 In-browser and offline AI 🟠 Undocumented Hacker News features 🐋 DeepSeek censored...
Dev Digest 198: 30 years of JS, In-Browser AI, How Attackers Abuse GenAI

From learning to earning

Jobs that call for the skills explored in this talk.