The Fast Path to Scale AI and Data Science Everywhere
Thousands of companies across industries are making artificial intelligence (AI) breakthroughs using existing systems enhanced with Intel® AI technologies. Through built-in hardware acceleration and optimizations for popular software tools, the AI workflow is now streamlined from data ingest to deployment at scale. For innovators using AI to take on great challenges, Intel is clearing the path forward to scale AI everywhere.
Going from concept to real-world scale quickly while minimizing costs and maximizing return means:
- Building with what you already know
- Leveraging existing technology
- Transforming data into actionable insights
- Building and deploying AI applications at scale
With hardware and software to optimize AI data, modeling, and deployment lifecycle, as well as accelerate analytics at every stage, Intel can help accelerate your time to insight. Where you go, so does Intel® AI.
Featured Use Cases
While companies use AI in different ways, they all face a common AI challenge—how to get from concept to real-world scale fast, with the least cost and risk. The following customers discovered that wherever they needed AI, their familiar Intel-based environment delivered.
UAB Medicine Uses AI to Access New Data
UAB Medicine and Medical Informatics Corp. work to enhance near-real-time decision-making and patient care.
SM Supermalls Engages Shoppers
Customer-facing New Era AI Robotic solution helps simplify and personalize each customer’s visit using Intel® technologies.
SDSC Builds AI-Focused “Voyager” Supercomputer
The AI-focused system allows scientists to develop new approaches for accelerated training and inferencing.
More Use Cases
Learn about more businesses that are delivering results with AI and data science on Intel, spanning use cases and industries from edge to cloud.
Developer Zone
Access development tools and resources to prepare, build, deploy, and scale your AI.
Learn more
Technology Showcase
Explore general purpose to domain-specific AI processors and additional portfolio technologies.
See AI hardware
Deployment Solutions
Discover a robust ecosystem of solutions to deploy AI from edge to cloud.
Deploy powerful AI
AI Learning Center
Discover Intel’s robust resources, training, and best practices around AI and data science.
What’s New with Intel® AI?
Get current with the latest blogs on everything AI and analytics.
AI 101
Learn about the relationship between AI, machine learning, and deep learning in this article.
What Is Data Analytics?
From basic data visualization to real-time predictive intelligence, there are countless ways to gain insight from the data you’re collecting.
Maximize the Data Analytics Pipeline
A well-constructed data pipeline supports business intelligence, trend identification, and data analysis at scale. Intel® technologies deliver the performance to create scalable, reliable, and actionable analytics programs.
Database Management
How you manage data makes a big difference in its potential value to your organization. Explore the qualities of a good data management strategy.
Intel® AI News
Advancements in AI are happening daily. Read the latest news about Intel’s impact on and involvement in the future of AI.
The Total Economic Impact of Intel® AI
Intel commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study and examine the potential benefits enterprises may realize by deploying Intel® AI.
Future of AI
Intel researchers are always striving to advance and shape the next decade of computing. Learn more about Intel Labs’ current initiatives.
Self-Paced AI Training
Learn AI concepts and follow hands-on exercises with free self-paced courses and on-demand webinars that cover a wide range of AI topics.
Frequently Asked Questions
What are the differences between machine learning, deep learning, and AI?
Artificial intelligence (AI) refers to a broad class of systems that enable machines to mimic advanced human capabilities. Machine learning (ML) is a class of statistical methods that uses parameters from known existing data and then predicts outcomes on similar novel data, such as with recession, decision trees, state vector machines. Deep learning (DL) is a subset of ML that uses multiple layers and algorithms inspired by the structure and function of the brain, called artificial neural networks, to learn from large amounts of data. DL is used for such projects as computer vision, natural language processing, recommendation engines and others.
What are the steps of turning data into valuable insights using AI?
Initially, data is created and entered into the system, at which point it goes through preprocessing to ensure consistent data form, type, and quality. When clean data is assured, it goes into a modeling and optimization process to support smarter, faster analytics. Once the AI model is proven, it can be deployed to meet project requirements.
How does analytics relate to artificial intelligence?
Analytics curve large amounts of data into patterns to predict future outcomes. AI automates data processing for speed, pattern discovery, and surfacing data relationships which then yield actionable insight.
Is a GPU necessary for AI projects?
No. Graphics processing units (GPUs) have historically been the choice for AI projects because they can handle large datasets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless running complex deep learning on extensively large datasets, CPUs are more accessible, more affordable, and more energy-efficient.
Is a GPU necessary for AI projects?
No. Graphics processing units (GPUs) have historically been the choice for AI projects because they can handle large datasets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless running complex deep learning on extensively large datasets, CPUs are more accessible, more affordable, and more energy-efficient.