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Intel Solutions for Classical Machine Learning

Learn how to use the Intel® portfolio of hardware, software, and partner solutions to maximize performance, lower costs, and accelerate your classical machine learning (ML) efforts.

Classical Machine Learning Takeaways

  • ML analyzes massive data sets to find patterns and make predictions about new data.

  • ML models are used in almost every industry for use cases such as behavior-driven marketing, fraud identification in banking, and social media recommendations.

  • Regulated industries often use ML instead of deep learning because it allows for transparency and explainability.

  • Intel provides hardware and software technologies that are optimized to accelerate ML pipelines from training to deployment.

  • Classical ML can often be performed on Intel® processors without the need to purchase a discrete GPU.



What Is Classical Machine Learning?

To gain a competitive edge, many of today’s businesses are implementing classical machine learning (ML), a subset of artificial intelligence (AI), across their organization. Classical ML uses models, or algorithms, to analyze massive data sets, identify patterns, and make predictions without human intervention. Organizations use ML-identified patterns and trends to make smarter, faster decisions that can improve business efficiency, improve security, and create new data-driven products and services tailored to customer behaviors. Common ML models include linear regression, logistic regression, support vector machines, nearest neighbor similarity search, and decision trees.

Classical ML models are often computationally lighter than deep learning neural networks. They rely heavily on the quality of the data they learn from and are considered explainable AI. Explainable AI gives organizations, decision-makers, and data scientists traceable insight into how an algorithm arrived at a specific result. With transparency into how the algorithm works, users can identify potential biases and discover how variables contribute to an outcome. Explainable AI is often required for regulated industries such as financial services and government. The explainable use of AI is also one of six principles that guides Intel’s internal multidisciplinary Responsible AI Advisory Council. Read more about Intel’s commitment to responsible AI.

No matter if you’re just starting ML, embarking on more-ambitious and advanced projects, or wanting to optimize the Intel® hardware you’re currently using for ML, we’re here to help you achieve success.

Classical Machine Learning Use Cases

Classical ML models are used for a variety of real-world applications across the financial services, health and life sciences, retail, research, and manufacturing industries. For example, financial institutions can build, train, and implement ML models to identify and predict fraudulent credit card transactions faster and more accurately, reducing the amount of money lost annually to fraud and better protecting sensitive customer information. Other popular ML use cases include customized marketingvisual quality control in manufacturing, personalized medicine, and retail demand forecasting.

Solving Classical Machine Learning Challenges with Intel® Technologies

Benefits abound for businesses that embrace classical ML. Smarter, faster decision-making. Operational improvements. More-efficient, more-effective business processes. New market opportunities. However, realizing these classical ML benefits can be a challenging and time-consuming journey for organizations and their AI team members.

To help simplify your ML initiative, let’s examine the three most common challenges we hear from our customers when building and running their ML pipelines. We’ll also provide recommendations about the Intel® hardware and software solutions—some of which you may already own and use—that can simplify and accelerate your success, as well as specific steps you can take today to help overcome the obstacles you’re facing.

Challenge No. 1: Completing Data Preparation Can Be Arduous, Inefficient, and Time Consuming

Data prep—the explorative and analytical steps that lead to classical machine learning—is one of the most critical parts of the AI life cycle because it ensures models are built on high-quality data. Yet preprocessing is often regarded as one of the most frustrating, time-intensive, and difficult parts of working in AI. And as the demand for machine learning rapidly grows, so will the workload of data scientists.

That’s why it’s more important than ever to look for opportunities to streamline and accelerate data science and AI pipelines. With the right combination of hardware and software solutions, you can significantly improve data science efficiency—across data ingress, exploration, and preprocessing.

Solution: Boost Data Scientist Productivity with Optimized Frameworks, Libraries, and Toolkits

Simplify and accelerate the development of your AI pipeline by leveraging machine learning optimizations on Intel® processors. Some of the optimized data science resources we offer to help you go from data to insights faster include:

  • Intel® AI Quick Start Guide Download this curated guide for fast and convenient access to all Intel-optimized AI libraries and frameworks for machine learning.
  • Optimized frameworks: We optimized two popular machine learning frameworks—scikit-learn and XGBoost—to increase their performance by 10x to 100x on Intel® hardware. These performance gains mean data scientists, AI developers, and researchers can be more productive without the need to learn new APIs or low-level foundational libraries.
  • Libraries and tools: Explore our comprehensive portfolio of tools and libraries that enable faster development, training, and deployment of machine learning solutions. All tools have been optimized for performance and productivity and are built on our standards-based, unified oneAPI programming model.

Data scientists, AI developers, and researchers can download the Intel® AI Analytics Toolkit (AI Kit), for easy access to our optimizations in one location. This toolkit is designed to maximize performance from preprocessing to machine learning.

Solution: Increase Performance for Compute-Intensive Workflows

Manipulate, explore, and optimize data faster with our selection of high-performance, workload-matched processors for your next data science workstation. Our processors can run medium-to-large data sets in memory to cut hours off your most time-consuming ML tasks.

Explore available CPU options for Intel®-based Data Science Workstations:

Challenge No. 2: Implementing AI across Multiple Hardware Architectures Is Costly and Complex

The costs of running, building, and deploying AI can add up quickly. After all, creating highly accurate, highly responsive machine learning solutions requires significant investments—in development, training, deployment, and maintenance. Plus, the complexity of your solution, the size of your data sets, and other variables like industry regulations greatly influence the amount of compute power you will need.

Solution: Maximize the Value of the CPUs You Already Have

With a broad hardware portfolio of processors and integrated accelerators, Intel makes it easier for you find a cost-effective solution or maximize the value of your existing CPUs to meet your project and budget needs—without requiring the purchase of an external GPU.

Getting more from your CPU also helps you get more from common tools for classical ML, including scikit-learn, which doesn’t support the use of GPUs or GPU acceleration.

Let’s look at some of the cost-effective Intel® solutions you can take advantage of.

  • Intel® Xeon® Scalable Processors: Designed to handle the most demanding AI workloads, these CPUs offer a larger memory capacity for the large data sets required for classical machine learning. With integrated Intel® Accelerator Engines purpose-built to maximize performance and efficiency for the most demanding, compute-intensive workloads, you can extract more value from your investment without the need for additional specialized hardware purchases.
  • Intel® Data Science Workstations: One machine that resides locally and combines large memory span, multiple expansion slots for more device connectivity. This system also includes a CPU designed to handle the demands of data science tasks and can be optimized, when needed, to avoid the purchase of an external GPU.
    Intel®-based data science workstations are offered in three platform options—mobile, mainstream, and expert. They can Intel® Core™ Processors, Intel® Xeon® W processors, or Intel® Xeon® Scalable processors and come in a variety of configurations and price ranges to align your performance needs with your budget. Intel® Data Science Workstations ship from our partners and manufacturers DellZ by HP, and Lenovo.

Challenge No. 3: Staying Compliant in Regulated Industries

Adopting ML in regulated industries presents many challenges. Strict compliance regulations, data privacy concerns, the need to ensure data is accurate and complete, AI explainability, and security requirements make using certain ML techniques difficult in industries such as healthcare, finance, and the public sector.

Solution: Keep Data On-Site and Protected with Powerful Workstations and Enhanced Security Capabilities

Financial services, healthcare, and the public sector are constantly evolving, while remaining highly regulated. These dynamics make it challenging to create innovative machine learning solutions quickly while ensuring compliance.

Intel has years of experience within these industries and builds solutions with their exact requirements in mind, including:

  • Intel® Data Science Workstations: Perform data ingress, exploration, and preprocessing on local data with on-site compute—ensuring your data sets stay firmly behind your firewall. When using your workstation on-premises, you can configure up to 8 TB of memory in dual-socket systems with workload-matched CPUs, so you can run large data sets without requiring data transfers or downsampling while showing due diligence in model selections. In addition to meeting your industry’s regulations, keeping your data on-premises can also help avoid the added costs that come with moving data in the cloud.
  • Hardware-based security features: Intel® CPUs come equipped with security measures to help you protect sensitive data and AI models and comply with regulations.
    Our foundational security features focus on identity and integrity. Intel® Boot Guard, Intel® Total Memory Encryption (Intel® TME)Intel® Platform Firmware Resilience (Intel® PFR), and other security technologies built in at the silicon level help ensure your platform boots correctly and runs as expected.
    To strengthen the security of workloads and data, we layer on enhanced security technologies, such as Intel® Software Guard Extensions (Intel® SGX), that protect virtual machines and operating systems against targeted attacks.
    Finally, our CPUs also include protections against emerging software attacks. Altogether, these layers of security measures help you stay compliant with federal data and privacy regulations.

The Full Intel® AI Portfolio

Explore our full portfolio of AI technologies, optimized resources, and partner solutions that create the robust, end-to-end architecture that all AI initiatives require—from classical machine learning to computer vision to generative AI.

Let Us Help Take Your ML Initiatives Further on the Platforms You Already Own

We’re here to help simplify your path to finding a machine learning solution that can solve your pressing business challenges. Our technology leadership, expertise, and decades of investment in ML optimizations on Intel® processors help accelerate your AI efforts while maximizing the value of your CPU and GPU investments.

We work with our ecosystem of solution partners, system integrators, technology vendors, and AI professionals to bring you innovative solutions that enable you to find connections, make predictions, and generate valuable insights faster and easier for your business.

Reach out to your Intel representative to accelerate your AI adoption.

Frequently Asked Questions

Classical machine learning is a powerful business analytics and explainable AI tool that’s used by various AI practitioners across a wide range of industries. Classical machine learning uses algorithms to analyze large sets of data to provide insights into identified patterns and trends. Examples of classical machine learning models include linear regression, logistic regression, support vector machines, nearest neighbor similarity search, and decision trees.