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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

Run AI models in the browser: Real life scenarios and implementations

Alexandru Hang

Why rely on remote servers when running AI directly in the browser unlocks zero-latency processing and strict data privacy? Master TensorFlow.js to deploy offline-ready predictive features client-side.

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#1 about 4 min

Pros and cons of running AI models in the browser

Developers must evaluate trade-offs like low latency and offline capabilities against compute constraints.

#2 about 3 min

Real-world applications and use cases for client-side AI

Modern applications leverage local processing for real-time video, face recognition, and on-device translation.

#3 about 2 min

Introduction to TensorFlow.js for browser machine learning

TensorFlow.js enables developers to train and run machine learning models directly in JavaScript environments.

#4 about 5 min

Basic concepts of training machine learning models

Machine learning models learn to classify data through structured training datasets and rigorous accuracy validation.

#5 about 3 min

Common machine learning algorithms and their structures

Fundamental machine learning structures include linear regression, decision trees, random forests, and neural networks.

#6 about 6 min

Implementing a linear regression model with TensorFlow.js

JavaScript code defines sequential models with hidden layers to predict values efficiently.

#7 about 4 min

Feeding data and avoiding overfitting during model training

Providing sufficient training examples prevents overfitting without suffocating the model or decreasing accuracy.

#8 about 5 min

Measuring accuracy with loss functions and epochs

Metrics like mean absolute error and mean squared error track model improvement across multiple training epochs.

#9 about 5 min

Building a neural network to predict customer purchases

A practical exercise demonstrates how to build a neural network that predicts customer purchases based on demographic data.

#10 about 1 min

Exploring pre-trained models and advanced AI resources

Pre-trained models provide accessible solutions for speech recognition, toxicity filtering, and text generation directly in the browser.

Matching moments

1:31 min

Overview of machine learning in the browser with TensorFlow

Håkan Silfvernagel · LIVE

1:33 min

Real-world implementations of browser-based machine learning

Håkan Silfvernagel · LIVE

2:46 min

The case for native AI in web browsers

Maxim Salnikov Maxim Salnikov · WWC 2025

2:54 min

Leveraging Chrome AI and nano models for web applications

Raymond Camden · Perf + AI

1:39 min

Advantages of running machine learning directly in the browser

Håkan Silfvernagel · LIVE

51 sec

Adopting hybrid approaches for browser-based AI models

Jason Mayes · Coffee With Developers

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