Premier AI Development Resources
Explore these deployable AI applications or build on top of the open source code.
Generative AI
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.
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.
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
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.
Intel Models on Hugging Face*
OpenVINO™ Toolkit Notebooks
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
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
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
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
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
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.
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).
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*.
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.
Build Secure Kubeflow* Pipelines on Microsoft Azure*
Leverage Intel® Software Guard Extensions on Virtual Machines to Deploy Secure, Accelerated Machine Learning Pipelines
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.
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.
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).
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.
Build Secure Kubeflow* Pipelines on Microsoft Azure*
Leverage Intel® Software Guard Extensions on Virtual Machines to Deploy Secure, Accelerated Machine Learning Pipelines.
Handwritten Digit Classification
In this example we will be recognizing handwritten digits using a machine learning classification algorithm.
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.
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.
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.
Custom AI Assistant
Learn how to run Llama 2 inference on Windows* and Windows Subsystem for Linux* (WSL2) with Intel® Arc™ A-Series GPU.
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.
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).
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.
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).
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.
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.
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.
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.
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.
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
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.
The model analyzes the images to build a database of application-specific knowledge.
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
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.
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.
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.
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.
Using Monocular Depth Estimation to Mask an Image
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.
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.
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
Data
Data engineering at scale:
Vector embedding:
Vector databases:
Develop
Pretrained models:
Distributed training and fine-tuning:
RAG and agents:
Optimize & Deploy
Model optimization:
Inference serving:
Monitoring:
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).
Data
Data engineering at scale:
Datasets
- Image classification dataset
- Roboflow* 100 object detection benchmark
- VALERIE22 urban environment synthetic dataset
Develop
Pretrained models:
Frameworks:
Training and fine-tuning:
Optimize & Deploy
Model optimization:
Inference serving:
Monitoring:
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.