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JavaScript Congress • Nov 25, 2021

Making neural networks portable with ONNX

Ron Dagdag

Struggling to deploy Python models in Java or JavaScript? ONNX acts as a universal translator for neural networks. Decouple training from deployment and ship AI to the edge seamlessly.

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#1 about 3 min

Establishing introductory context regarding artificial intelligence deployments

Framing context around artificial intelligence deployment capabilities establishes foundational concepts for developers transitioning operational logic structures.

#2 about 3 min

Differences between traditional programming and machine learning algorithms

Providing structured data as training examples allows programmatic pipelines to automatically write an algorithm solving a core problem.

#3 about 4 min

Bridging application frameworks with the universal ONNX format

Creating universal model formats allows artificial intelligence capabilities to run seamlessly across completely disconnected server architectures.

#4 about 3 min

Ideal system architectures and use cases for portability

Removing heavy dependencies reduces computational latency making models ideal for constrained environments running directly on hardware endpoints.

#5 about 5 min

Sourcing and generating pre-trained machine learning models

Bootstrapping software capabilities via public model repositories accelerates immediate project value avoiding prolonged dataset configuration overhead.

#6 about 2 min

Visualizing model operation graphs with the Netron application

Inspecting loaded payload architectures illuminates proper array dimensions and variable names needed when coding inference execution scripts.

#7 about 6 min

Converting existing application architectures into interoperable runtime graphs

Recompiling localized scripts into universally accessible payloads unifies continuous integration infrastructure when managing experimental operational variants.

#8 about 7 min

Comparing cloud compute infrastructure against local edge deployment

Distributing computation targets closer to client networks lowers cloud ingestion fees and resolves critical bandwidth dependency factors.

#9 about 2 min

Integrating capabilities through high performance ONNX runtime engines

Abstracting execution paths ensures analytical computations fall gracefully back on existing hardware graphic processors determining automatic render speeds.

#10 about 4 min

Writing data payload mappings inside Node backend instances

Translating primitive numbers into valid session instances enables raw backend environments securely interpreting loaded runtime regression logic definitions.

#11 about 3 min

Running independent inference graphs securely on client browsers

Isolating execution evaluations natively within standard browser sessions protects client transmission events and avoids continuous architectural hosting limits.

#12 about 7 min

Processing browser image states into acceptable neural formats

Programmatically resizing source images matching specified training dimensions accurately transforms raw inputs correctly predicting contextual emotion markers.

#13 about 2 min

Optimizing graph layers targeting constrained mobile execution environments

Optimizing parameter weight scales tightens application boundaries preventing bloated bundle downloads restricting underlying runtime mobile execution capabilities.

#14 about 10 min

Addressing audience questions on entering data science domains

Exploring straightforward remote interfaces builds comfortable developer understanding before studying complex foundational computation architecture initially required completely.

Matching moments

2:57 min

Standardizing interoperable model deployments with the ONNX framework

Jose Luis Latorre Millas · LIVE

1:55 min

Exporting framework independent representations for edge processing platforms

Nico Schmidt · LIVE

2:15 min

Open-source community and machine learning frameworks

Gian Marco Iodice Gian Marco Iodice · WWC 2025

2:46 min

Leveraging ONNX Runtime Web for local model execution

Maxim Salnikov Maxim Salnikov · WWC 2025

4:47 min

Exporting and evaluating learned ONNX models

Marco Zamana · LIVE

57 sec

Comparing ONNX runtime web and TensorFlow deployments

Jason Mayes · Coffee With Developers

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