📚 Jupyter notebook tutorials for OpenVINO™

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Inference and fine-tuning examples for vision models from 🤗 Transformers

Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!

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This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

MLOps on AWS using Amazon SageMaker Pipelines

Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6

A collection of Kotlin-based examples featuring AI frameworks such as Spring AI, LangChain4j, and more — complete with Kotlin notebooks for hands-on learning.

This repo contains information regarding cloud offerings of OpenVINO™ and demos to showcase OpenVINO™ via sample Jupyter notebooks.

End-to-end YOLOv8 PPE detection for construction-site safety. Includes Jupyter training notebooks, pretrained weights, dataset (Roboflow), inference/testing scripts, and a Flask web dashboard for real-time monitoring and compliance reporting.

A useful collection of notebooks for quantization, fine-tuning, and inference with the Turkish LLaVA model.

Colab notebooks written for the ai course

Benchmarking notebooks for various Persian G2P models, comparing their performance on the SentenceBench dataset, including Homo-GE2PE and Homo-T5.

Minimal implementation of how you can do TensorFlow 1.15-based object detection inference in a Google Cloud Function

An interactive marimo notebook that uses ICICLE AI Tapis services for a hands-on RAG (retrieval-augmented generation) playground.

Jupyter Notebooks examples on how to use Scaleway AI Inference solution (Early Access)

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

Example scripts and notebooks showcasing how to use DerivKit’s tools for analysis, differentiation, and forecasting.