Machine Learning
Machine learning is transforming the way businesses operate. From customized marketing and fraud detection to supply chain optimization and personalized medicine, machine learning turns complex data into real-world insights.
Montefiore Health Systems uses machine learning and AI applications from the Intel® AI software portfolio to help predict and prevent chronic health issues in patients.
How Machine Learning Works
Machine learning is a subset of AI that uses models, or algorithms, to analyze large amounts of complex data and identify patterns. Models use historical data to make predictions about new data with minimal human intervention.
1. Explore
The model analyzes the dataset to uncover patterns and identify any data that might need to be cleaned or preprocessed.
2. Train
The model analyzes the data to learn the best combination of hyperparameters to minimize prediction error.
3. Infer
The model uses inference to make predictions about previously unseen data.
4. Monitor
Observe model performance to identify any model drift that might require the model to be updated or retrained.
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.
Health and Life Sciences: Automate Visual Quality Control Inspections for Life Sciences
Financial Services: Credit Card Fraud Detection
Types of Machine Learning
There are three types of machine learning: supervised, unsupervised, and reinforcement. The type used depends upon the kind of data being analyzed and the task to be accomplished.
Supervised Learning
This method uses labeled datasets that are trained to identify specific target variables. There are two categories of supervised learning: classification, which is used to predict categorical outcomes like the quality of a product, and regression, which is used to predict continuous outcomes like the number of products sold.
Unsupervised Learning
This method is used when input datasets are not labeled, and is used to uncover patterns and commonalities within the data. Use cases include clustering and segmentation, association rules, and dimensionality reduction.
Reinforcement Learning
With this method, an input dataset is not required; data is generated through a feedback system during training. The model continuously learns through feedback from previous actions. Robotics and gaming commonly use this method to maximize the cumulative reward based on past experiences.
Real-World Example: Machine Learning for Customer Segmentation
A large, multinational retailer wants to create a series of targeted marketing campaigns. Using customer segmentation, they can quickly identify shoppers with shared purchasing characteristics.
1. Explore
Cleanse online purchase transactions from a multinational retailer and visualize them.
2. Train
Apply unsupervised learning to train AI-based clustering algorithms to identify critical transactions and customer segmentation categories. Notice that the green clusters are separate from the red clusters, indicating distinct customer segments.
3. Infer
Input new customer purchase transactions into the clustering algorithms.
4. Monitor
The clusters move together and become less distinguishable as the model drifts over time.
Intel® AI Software Portfolio
Intel offers an end-to-end AI software portfolio for use cases across computer vision, natural language processing, audio, and recommender systems.
AI Frameworks
All major frameworks for deep learning and classical machine learning have been optimized by using oneAPI libraries that provide optimal performance across Intel CPUs and XPUs. These Intel software optimizations help deliver orders of magnitude performance gains over stock implementations of the same frameworks.
AI Tools, Libraries & Framework Optimizations
Intel provides a comprehensive portfolio of tools for all your AI needs, including data preparation, training, inference, deployment, and scaling. All tools are built on the foundation of a standards-based, unified oneAPI programming model with interoperability, openness, and extensibility as core tenets.
AI Platform for Developers
Intel is empowering developers to run end-to-end AI pipelines with Intel® Xeon® Scalable processors. From data preprocessing and modeling to production, Intel has the software, compute platforms, and solution partnerships you need to accelerate the integration of AI everywhere.
Recommended Resources
Introduction to Machine Learning
This course provides an overview of machine learning fundamentals on modern Intel architecture.
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How Smart Enterprises Get Ahead with Machine Learning
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