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Premier AI Development Resources

Explore these deployable AI applications or build on top of the open source code. 


 

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Generative AI

Generative AI (GenAI) models create new content such as images, text, audio, or video based on their training data. Large general-purpose models require extensive resources to train from scratch, but you can customize these foundation models with less effort using fine-tuning or retrieval based on a private dataset.

Machine Learning

Machine learning turns complex data into real-world insights , from customized marketing and fraud detection to supply chain optimization and personalized medicine. Choose the right type of machine learning for your application and begin developing using software optimized for Intel hardware.

Computer Vision

Computer vision is a field within AI that gives machines a human-like ability to see and understand the world. This cutting-edge technology is transforming lives by enabling a wide range of devices to extract meaningful data from digital images and video.


 

 

Latest Models, Kits, Applications and More

DeepSeek* and Small Scale Model Performance

DeepSeek-R1, a new reasoning model, bridges the gap between large-scale performance and local deployment by offering advanced reasoning capabilities in a small language model (SLM).

  • Advanced Reasoning: Efficiently transfers complex reasoning to SLMs for local deployment.
  • Innovative Techniques: Uses Group Relative Policy Optimization (GRPO) and chain-of-thought (CoT) reasoning.
View Findings
Custom AI Assistant

Use speech recognition and natural language processing (NLP) in a voice-activated tool that can respond with annotated summaries, actionable recommendations, and insight. 

  • Natural language processing, speech recognition 
  • Uses Llama 2 or later and Distil-Whisper 
  • Use cases include healthcare, retail, and finance 
View on GitHub
ChatQnA Application

This ChatQnA use case performs RAG using LangChain, Redis VectorDB and Text Generation Inference on Intel Gaudi2 or Intel XEON Scalable Processors. The Intel Gaudi2 accelerator supports both training and inference for deep learning models in particular for LLMs. 

View on GitHub

Social Counter-Factuals Vision-Language Model Dataset
View on GitHub
Intel Models on Hugging Face*

Computer vision is a field within AI that gives machines a human-like ability to see and understand the world. This cutting-edge technology is transforming lives by enabling a wide range of devices to extract meaningful data from digital images and video.

Browse the Models
OpenVINO™ Toolkit Notebooks

A collection of ready-to-run Jupyter notebooks for learning and experimenting with the OpenVINO™ Toolkit. The notebooks provide an introduction to OpenVINO basics and teach developers how to leverage our API for optimized deep learning inference.

Browse All
Defect Detection

This comprehensive solution to quality control uses computer vision and unsupervised learning for anomaly detection to catch manufacturing defects in real time.

  • Detects anomalies
  • Uses the Anomalib* deep learning library
  • Use cases include manufacturing, healthcare, and agriculture 
View on GitHub
Explainable AI

Use data quality measurements and saliency maps to understand the predictions of computer vision models, helping refine them for efficiency and performance.

  • Data and model explainability
  • Uses YOLOv8 or later
  • Use cases include transportation, healthcare, and retail 
View on GitHub
Automated Self-Checkout

Use computer vision to detect, count, and track objects within specified zones, such as retail shelves and point-of-sale stations, while providing real-time analytics data.

  • Object detection and tracking
  • Uses YOLOv8 or later and supervision
  • Use cases include retail business
View on GitHub
Intelligent Queue Management

Manage queues using computer vision and object detection. This solution incorporates real-time data to optimize queues and reduce wait times. 

  • Object detection and counting
  • Uses YOLOv8* or later
  • Use cases include retail business, and healthcare 
View on GitHub
Smart Meter Scanning

Automate meter reading using computer vision and segmentation to transform analog data into digital data. It is useful for any industry that uses analog meters. 

  • Object detection and segmentation
  • Uses AI models from PaddlePaddle
  • Use cases include energy and manufacturing 
View on GitHub
Build an End-to-End Machine Learning Workflow

Download and try this code sample, covering data preparation, model training, and ridge regression using US census data.

View Workflow
DBSCAN for Spoken Arabic Digit Dataset
View on GitHub
Kmeans for Spoken Arabic Digit Dataset
View on GitHub
Ridge Regression for Airlines DepDelay dataset
View on GitHub
XGBoost Classification Model Using daal4py prediction

This sample demonstrates how to train and predict with a distributed linear regression model using the Python API package Daal4py powered by the Intel® oneAPI Data Analytics Library (oneDAL).

View on GitHub
SVC for Adult Data Set Sample

SVC for Adult Data Set sample uses the Adult dataset to show how to train and predict with an SVC algorithm using Intel® Extension for Scikit-learn*.

View on GitHub

Use Cases

Machine learning is used in a broad range of use cases. Companies that integrate machine learning applications into their processes often see substantial competitive advantages such as increased revenue, lower costs, and more efficient operations.

How Smart Enterprises Get Ahead with Machine Learning

 

Anomaly Detection
Analyze large amounts of data, often streaming in over time, to identify significant deviations or outliers that could indicate potential problems.
Anondot* Optimizes Anomaly Detection Services

Prediction
Analyze large amounts of data, often streaming in over time, to identify significant deviations or outliers that could indicate potential problems.
Supply Chain Late Delivery Prediction

 

Classification
Categorize new observations using a model trained on a multi-variable labeled dataset.
Diagnose Mental Health Conditions from Brainwave Data

 

Segmentation
Combine attribute and behavioral data over time to create groups based on relevant characteristics.
AI-Based Customer Segmentation

 


Featured Tutorials
Introduction to Machine Learning

This course provides an overview of machine learning fundamentals on modern Intel architecture.

View Course
Build Secure Kubeflow* Pipelines on Microsoft Azure*

Leverage Intel® Software Guard Extensions on Virtual Machines to Deploy Secure, Accelerated Machine Learning Pipelines

Read in Parallel Universe Magazine
Optimize Utility Maintenance Prediction for Better Service

Utility service reliability depends on the health of the assets in the field. As energy consumption continues to grow worldwide, distribution assets in the field are also expected to grow. In the US, for example, the failure of any of the 170 million utility poles could result in considerable service disruptions and potentially risk creating wildfires in vulnerable areas.

Read on Medium*
Gradient Boosting with Intel® Optimization for XGBoost*

This article focuses on the XGBoost algorithm and compares its performance to related tree-based models. We can access the XGBoost algorithm as a Python package (xgboost) using Anaconda*, Python pip, or other package managers. We install the relevant dependencies step-by-step as we progress through this tutorial.

Read
Distributed Linear Regression

This sample demonstrates how to train and predict with a distributed linear regression model using the Python API package Daal4py powered by the Intel® oneAPI Data Analytics Library (oneDAL).

View on GitHub
How Smart Enterprises Get Ahead with Machine Learning

Machine learning helps organizations improve and reinvent business processes, identify new market opportunities, and mitigate known and unknown risks.

Learn More
Build Secure Kubeflow* Pipelines on Microsoft Azure*

Leverage Intel® Software Guard Extensions on Virtual Machines to Deploy Secure, Accelerated Machine Learning Pipelines.

Learn More
Handwritten Digit Classification

In this example we will be recognizing handwritten digits using a machine learning classification algorithm. 

View on GitHub

 

 

Use Cases

GenAI has the potential to revolutionize creative industries, enhance content generation processes, and drive innovation across industries and applications.

Simulation and Gaming
Construct virtual environments, characters, and scenarios that enhance realism and interactivity in simulation and gaming applications.

 

Healthcare and Medicine
Produce synthetic medical images or data that can be used to assist in medical research, diagnosis, and treatment planning.
GenAI Imaging Innovations in HealthTech

 

Natural Language Processing (NLP)
Build natural language generation, dialogue systems, and text synthesis for chatbots, language translation, and content summarization applications.
Fully Customizable Multilingual Expert

 

Personalization and Recommendation Systems
Deliver personalized recommendations, advertisements, or product suggestions based on user preference and behavior.
Transform Retail with RAG

 

Design Visualization
Render images of design ideas for a customer's home or office or of clothing options shown on a customer's avatar.
Transform Retail with RAG

 

A Use-Case-Specific Approach to Generative AI
Identify compounds that solve multiple objectives with an understanding of their underlying complex physical and chemical relationships.
HoneyBee: A State-of-the-Art Language Model for Materials Science

 

Materials Science and Drug Discovery
Render images of design ideas for a customer's home or office or of clothing options shown on a customer's avatar.
Watch the Video

 

 


Training, Tutorials and Certifications

Explore the possibilities of developing on the Intel® Developer Cloud with our curated list of AI tutorials.

GenAI Tutorials on the Intel Tiber Developer Cloud


A Developer's Guide to Getting Started with Generative AI

LLMs

With the ability to generate text, summarize and translate content, respond to questions, engage in conversations, and perform complex tasks such as solving math problems or reasoning, LLMs have the potential to benefit society at scale.

 

Llama* 2 Fine-Tuning with Low-Rank Adaptations (LoRA) on Intel® Gaudi®2 AI Accelerator

Learn how to fine-tune Llama 2 70B with DeepSpeed ZeRO-3 and LoRA* techniques on eight Intel® Gaudi® 2 AI accelerators.

View Tutorial
GenAI Essentials: LLM Inference

See LLM inference on the latest Intel Data Center GPU Max 1100 using two models; Camel 5B and Open LLaMA 3B v2l.

Read on Medium*
Custom AI Assistant

Learn how to run Llama 2 inference on Windows* and Windows Subsystem for Linux* (WSL2) with Intel® Arc™ A-Series GPU.

Read the Tutorial
Comprehensive Strategies for Optimization and Deployment with the OpenVINO™ Toolkit

Use the OpenVINO toolkit to compress LLMs, integrate them into AI-assistant applications, and deploy them for maximum performance, whether on edge devices or in the cloud.​

Download the PDF
Weight-Only Quantization to Improve LLM Inference on AI PCs

Take advantage of weight-only quantization (WOQ) to improve LLM inference using Intel® Extension for PyTorch* on AI PCs featuring Intel® Core™ Ultra processors (formerly code named Meteor Lake).

Read the Tutorial


 

Retrieval Augmented Generation (RAG)

RAG supplements pretrained models with up-to-date, proprietary, and confidential data from vector databases during inference. This simplifies customization and updating, and enables attribution of generated information to its source.

 

RAG Inference Engines with LangChain* on CPUs

Develop a unique vantage point on RAG — understanding its role and potential in scalable inference within production-scale LLM deployments.

View Tutorial
Create a RAG system using OpenVINO and LlamaIndex

Build AI applications that can reason about private data or data introduced after a model’s cutoff date by augmenting the knowledge of the model with the specific information it needs. The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG).

View on GitHub
Building Blocks of RAG: Harness the Value of Your Data with Domain-specific LLMs

Learn how retrieval augmented generation plus Intel hardware and software can help you yield incredible value and performance for your production GenAI models.

Download the Ebook


 

Image, Video, and Audio Generation

Image or video generation creates new content based on descriptive text or image input. Audio generation clones and creates voices, sounds, and music from text prompts or input audio. These techniques typically rely on diffusion or transformer models.

Controllable Music Generation with MusicGen and OpenVINO™ Toolkit

MusicGen is a single-stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.

View on GitHub
Stable Diffusion Inference on Intel Arc and Data Center GPUs

Set up and run a stable diffusion model using Intel® Arc™ Discrete Graphics and Pytorch or Tensorflow framework.

View on GitHub
Text-to-Video Generation with ZeroScope and OpenVINO Toolkit

The ZeroScope model is a free and open-source text-to-video model that can generate realistic and engaging videos from text descriptions.

View on GitHub
Text-to-Image Generation with Stable Diffusion* on Intel® Developer Cloud

These models take text as input and generate an image from that text prompt.

View Tutorial


 

Code Generation

GenAI can create or suggest new code snippets using natural language or text prompts. This technology can also translate code from one programming language to another and efficiently test and debug computer code.
 

Optimize Code Development with LLMs on the Intel Developer Cloud

Generate code more efficiently with optimized LLMs on Intel® Data Center GPU Max Series 

View Tutorial

Use Cases

Developers and data scientists across industries use Intel's computer vision and AI technology to build solutions that solve some of the world's toughest challenges.

 

Using Computer Vision to Perform a Wild Elephant Census

Computer vision has recently become a critical tool for wildlife conservation. In this example, data scientists used the technology to conduct a census of wild elephants. By inputting drone-captured video into a specially developed deep learning model, the scientists were able to quickly and accurately determine the size of the region's elephant population.

Object Recognition

Deep learning models must be trained to recognize specific objects. In this example, data scientists inputted thousands of labeled elephant images so the models could learn to identify the animal in the wild.

Image Classification

Trained deep learning models enabled the census application to differentiate between elephants and antelopes and correctly categorize each animal.

Object Detection

When the deep learning models detected an elephant, they highlighted the animal's position with a color-coded bounding box labeled with the appropriate classification.

Image Segmentation

Using color to distinguish pixels that form the image of the elephant, scientists gained more detailed information about the animal's position.

Medical Imaging - Streamline workflows to help improve patient care with faster and more accurate diagnosis.
Contouring for Radiation Therapy

 

Retail Operations - Automate inventory management, optimize product placement, and improve operational efficiency.
Learn More

 

Remote Monitoring - Monitor activity from closed-circuit cameras so city, building, or retail managers can make more informed operational decisions.
Video AI Assistant

 

Industrial Inspection - Automatically detect quality, safety, and productivity issues in real-time.
Automating Worker Safety

 

Human Motion Tracking - Analyze human movement in real-time to prevent injuries, improve athletic performance, and enable gesture-based interaction.
Human Motion Tracking System

 

Image/Video Processing - Upscale, enhance, or modify images or video streams.
Video Streaming Optimization

 


Training, Tutorials and Certifications


Computer vision mimics how the visual cortex of the human brain works. Like our brains, it can recognize and classify what it sees and even infer information from new data. But first, it must be trained using a type of AI called deep learning.

Input

The process of introducing labeled images into a deep learning model.

 


Train

The model analyzes the images to build a database of application-specific knowledge.

 

 


Interpret

The trained model can recognize and classify specific visual information.


Infer

The model uses inference to recognize and classify new visual information.


 

Featured Training:
A Step-By-Step Guide to Hunting Dinosaurs with Intel® AI

Create a Dinosaur Bone Likelihood Map and Use It to Discover New Dinosaur Fossils 

View Tutorial
Accelerated Image Segmentation using PyTorch*

Follow along with the steps to work with a satellite image dataset called SpaceNet5 and how to optimize the code to make deep learning workloads feasible on CPUs just by flipping a few key switches.

View Tutorial
Optimize PyTorch* Inference Performance on GPUs Using Auto-Mixed Precision

Learn how to perform PyTorch for ResNet*-50 model training and inference using the CIFAR-10 dataset on a discrete Intel GPU with Intel Extension for PyTorch.

View Tutorial
Train a Classifier on Intel® Gaudi™

Learn how to train a Convolutional Neural Network (CNN) to classify images. Start with CPU training, and then adjust the model on Gaudi HPU.

View Tutorial
Depth estimation with DepthAnything and OpenVINO

Depth Anything is a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, this project aims to build a simple yet powerful foundation model dealing with any images under any circumstances. 

View Tutorial
Using Monocular Depth Estimation to Mask an Image

Learn steps to create a clipped image with background clutter removed from the image using Monocular Depth Estimation. 

View Tutorial
Human Action Recognition with OpenVINO™

This notebook demonstrates live human action recognition with OpenVINO, using the Action Recognition Models from Open Model Zoo, specifically an Encoder and a Decoder.

View Tutorial
Person Tracking with OpenVINO™

This notebook demonstrates live person tracking by reading frames from an input video sequence, detecting people in the frames, uniquely identifying each one of them and tracking all of them until they leave the frame. 

View Tutorial


 

Intel® Certified Developer—MLOps Professional

Whether you are deploying an AI project into production or adding AI to an existing application, building a performant and scalable machine learning operations (MLOps) environment is crucial to maximizing your resources.

Explore the Certification

 

 

GenAI Software and Ecosystem

Example Application and Pipelines

  • Example GenAI Applications from OPEA
  • Tutorials for GenAI Essentials on Intel® Tiber™ Developer Cloud
  • Pipeline Repository for GenAI with the OpenVINO Toolkit
Data

Data engineering at scale:

  • Modin* for pandas
  • Apache Spark*

Vector embedding:

  • Text Embeddings Inference (TEI)
  • Sentence Transformers
  • SetFit

Vector databases:

  • Weaviate*
  • Redis*

     

Develop

Pretrained models:

  • Intel Models on Hugging Face
  • Intel Gaudi AI Accelerator Models

Distributed training and fine-tuning:

  • PyTorch* on Intel® Gaudi® Processors
  • DeepSpeed
  • Intel® Optimization for Horovod*

RAG and agents:

  • Haystack*
  • LangChain*
  • LlamaIndex*
  • fastRAG

 

Optimize & Deploy

Model optimization:

  • OpenVINO Toolkit
  • Hugging Face Optimum for Intel
  • Intel® Neural Compressor
  • Microsoft* Olive

Inference serving:

  • OpenVINO™ Model Server
  • vLLM
  • Text Generation Inference (TGI)

Monitoring:

  • Intel® Tiber™ AI Studio

 

 

Computer Vision Software and Ecosystem

OpenVINO™ Toolkit

This free, open source toolkit helps AI inferencing models run faster and makes them easier to deploy on your existing hardware. The OpenVINO™ toolkit was founded in computer vision—it's even in the name: Open Visual Inference and Neural Network Optimization.


Computer vision applications across industries can benefit from the performance enhancements provided by the OpenVINO toolkit, powered by oneAPI.


With a public or pretrained model, C++ and Python* developers can write once and deploy anywhere to continue their workflow using common frameworks. These frameworks include TensorFlow*, PyTorch*, and ONNX* (Open Neural Network Exchange).

Get it Now
Data

Data engineering at scale:

  • Modin* for pandas
  • Apache Spark*

Datasets

  • Image classification dataset
  • Roboflow* 100 object detection benchmark
  • VALERIE22 urban environment synthetic dataset

 

 

Develop

Pretrained models:

  • OpenVINO models on Hugging Face*
  • Intel Gaudi AI Accelerator Models

Frameworks:

  • PyTorch*
  • TensorFlow*

Training and fine-tuning:

  • Intel Gaudi Software
  • DeepSpeed*
  • Intel® Optimization for Horovod*

 

Optimize & Deploy

Model optimization:

  • Intel® Neural Compressor
  • Microsoft* Olive

Inference serving:

  • OpenVINO™ Model Server
  • Web Neural Network API (WebNN)

Monitoring:

  • Intel® Tiber™ AI Studio

Machine Learning Software and Ecosystem

Intel develops software in addition to contributing optimizations to open-source machine learning tools. This enable faster training and lower inference latency on various hardware while using your preferred tools.


Learn about Intel Tiber AI Studio, a full-stack machine learning operating system.

Explore
  • Modin* for pandas
  • Intel® Distribution for Python*
  • Apache Spark*
     
Train
  • scikit-learn*
  • XGBoost
  • LightGBM
  • CatBoost*
Infer
  • scikit-learn
  • Fast Tree-Inference for Gradient Boosting
Monitor
  • Deploy and Monitor with Intel® Tiber™ AI Studio (Formerly cnvrg.io*)
Example Applications and Pipelines
  • Tutorial for Machine Learning Using oneAPI on Intel® Tiber™ Developer Cloud
  • Intel® Optimized Cloud Modules

Stay Up to Date on AI Workload Optimization.


Sign Up

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