Pretrained AI Reference Kits
Use these reference kits to accelerate AI for your business.
Scale AI Faster with Technology You Know
Perform complex computations and run AI efficiently at scale with your current skills and infrastructure enhanced with Intel® AI technologies and partner solutions. Over 70 percent of successful AI inference deployments in the data center already run on Intel.1
Turn your AI ambitions into reality by leveraging an unmatched range of open source and free tools, libraries, optimized software frameworks, and our AI hardware portfolio—including built-in accelerators—for end-to-end machine learning pipelines. Access market-ready innovations through our partner ecosystem to address the full spectrum of AI requirements, from edge to cloud, for a range of use cases.
Solve Important Business Challenges with AI
From accelerating scientific research to improve patient outcomes to helping financial institutions identify fraud and mitigate risk to deliver better customer experiences, Intel is helping companies across industries harness the power of AI to solve today’s most complex challenges.
A Full Suite of Software and Hardware Technologies for the Entire Data and Machine Learning Pipeline
Get Fast Results with Flexible AI Hardware
Use the Intel® portfolio for AI hardware to support your project and infrastructure needs at every point in development through deployment.
Streamline AI Projects with Software Tools
Accelerate your AI development and optimize production performance with Intel® software tools and optimizations.
Improve Decision-Making with Advanced Analytics
Unlock maximum value with near-real-time insights from across the organizational data pipeline with high-performance hardware, optimized for the software you use.
Deploy AI from Edge to Cloud
Deploy AI workloads for the use cases that matter the most with an Intel-optimized environment that can be supported by our partner ecosystem.
Plan, Deploy, and Scale Intel® AI Solutions Designed for Fast Time to Value
Whether you’re looking for an existing AI solution or want to build one of your own, Intel can help accelerate your journey from concept to real-world success.
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Access development resources, including deep learning frameworks, optimization tools, and reference libraries, to help you prepare, build, deploy, and scale your AI solutions.
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FAQs
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 use parameters from known existing data and then predict outcomes on similar novel data, such as with recession, decision trees, and 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 transforms 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 data sets efficiently. However, today’s central processing units (CPUs) are often a better choice for AI projects. Unless you’re running complex deep learning on extensive data sets, CPUs are more accessible, more affordable, and more energy efficient.2