* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * What is machine learning? What is machine learning? ========================= Updated April 15, 2026•*5*-minute read Copy URL Jump to section --------------- What is machine learning (ML)?How does machine learning work?Training and evaluationDeploying ML modelsDeep learning and artificial neural networksHow machine learning can be appliedChallenges and considerationsHow Red Hat can help What is machine learning (ML)? ------------------------------ Machine learning (ML) is a subcategory of [artificial intelligence (AI)](/en/topics/ai) that uses algorithms to identify patterns and make predictions within a set of data. This data can consist of numbers, text, or even photos. Under ideal conditions, machine learning allows humans to interpret data more quickly and more accurately than we would ever be able to on our own. Machine learning is derived from mathematical foundations that enable algorithms to learn from data, make predictions, and optimize models. [Explore Red Hat AI](/en/products/ai) How does machine learning work? ------------------------------- Artificial intelligence develops when humans synthetically create a sense of human-like intelligence within a machine. For machine learning, this means programming machines to mimic specific cognitive functions that humans naturally possess, such as perception, learning, and problem-solving.  How do you get a machine to think like a human? You train it to create its own predictive model. This predictive model serves as the means in which the machine analyzes data and ultimately becomes a "learning" machine. To initiate this process, you’ll need to provide the computer with data and choose a learning model to instruct the machine on how to process the data. [Learn how AI can work for the enterprise](/en/topics/ai/what-is-enterprise-ai) A machine learning model can ultimately use data to serve 3 functions: * Describe what happened * Predict what will happen * Make suggestions about what action to take next The learning model chosen to train the machine is dependent on the complexity of the task and the desired outcome. Machine learning is typically classified by 3 learning methods: supervised machine learning, unsupervised machine learning, and reinforcement machine learning. **Supervised learning** algorithms are trained with labeled data sets. This model is used for tasks like image recognition. **Unsupervised learning** models look through unlabeled data and find commonalities, patterns and trends. This is used for tasks like customer segmentation, recommendation systems, and general data exploration. **Reinforcement learning** models are trained using a process of trial and error within an established reward system. This style of learning is used for things like training a computer to play a game where actions lead to a win or a loss.  Once the computer is familiarized with the way you want it to interpret data (thanks to the learning model and training data), it can make predictions and carry out tasks when presented with new data. Gradually, the computer will become more accurate with its predictions as it learns from continuous streams of data and be able to carry out tasks in less time and with more accuracy than a human could. Red Hat resources ----------------- [Keep reading](/en/resources "Keep reading") How does training and evaluation of machine learning models work? ----------------------------------------------------------------- The training phase of machine learning is when the model learns from a set of provided data. During this phase, developers aim to adjust the model’s parameters and minimize errors in its output. This is done by establishing a pipeline to pass data though the model, evaluate its predictions, and use the predictions to improve the model. That pipeline often embodies these steps: 1. **Collect and prepare data:** Data is collected and then prepared by separating into training data and testing data, removing unwanted data, and randomizing for even distribution. Reducing the number of input variables or features in a dataset while retaining its essential information is known as “dimensionality reduction.” 2. **Select a model:** Data scientists and engineers have created various machine learning algorithms for different tasks like speech recognition, image recognition, prediction, and more. 3. **Training:** The prepared input data is sent through the model to find patterns (pattern recognition) and make predictions. 4. **Evaluating:** After the training, a model’s output is evaluated against a previously unused set of data. Tuning: Developers then tune the parameters to improve further the model based on findings from the previous evaluation step. [Connect models to data and agents with Red Hat AI](/en/products/ai/connecting-models-to-data) ### Common challenges during training and evaluation A model performing well on the training data but poorly on the test data may be **overfitting**–learning too much from noise in the training data. A model that performs poorly on both sets may be **underfitting**–which occurs when it fails to learn the underlying patterns. [LoRA and QLoRA](/en/topics/ai/lora-vs-qlora) are resource-efficient fine-tuning techniques that can help users avoid overfitting. To ensure against overfitting the training data, a separate validation data set may be used. After each iteration, the model's output is evaluated against the validation data. Adjustments are then made to prevent overfitting. This is the application of dimensionality reduction: removing the extraneous data that can lead to overfitting. This reduction must be done carefully so as not to lead to underfitting. To correct for underfitting, developers must add more informative features to improve the model’s ability to capture complex relationships in the data. **Data leakage** occurs when information from the test set accidentally leaks into the training set, giving an unfair advantage and resulting in overestimated performance. Tuning, new features, and more relevant data can minimize errors on future iterations. Deploying ML models ------------------- When ML models are ready to deploy, they run a process called [inference](/en/topics/ai/what-is-ai-inference). Inference is the “action phase” when an AI model applies what it’s learned during training to provide outputs in a production environment. [vLLM](/en/topics/ai/what-is-vllm) is an inference server and engine that works to make the process of inference more efficient. It uses memory management techniques to maximize processing speed (throughput) so users can get fast and reliable results from their ML models. [What is Red Hat AI Inference](/en/products/ai/inference) How are deep learning and artificial neural networks used in machine learning? ------------------------------------------------------------------------------ Neural networks are a type of algorithm used in machine learning. They are particularly suited for tasks involving complex, non-linear relationships in data. [Deep learning](/en/topics/ai/what-is-deep-learning) is a subset of machine learning that uses neural networks many layers deep. These deep neural networks are well-structured to learn hierarchical representations of data. This makes deep learning extremely powerful for tasks like image recognition, natural language processing, and speech recognition. Find out how Mixture of Experts (MoE) models use deep learning and neural networks to speed up AI inference. [Learn how MoEs work](/en/topics/ai/mixture-of-experts) How machine learning can be applied ----------------------------------- Machine learning and artificial intelligence can be used to enhance user experience, anticipate customer behavior, monitor systems to detect fraud, and can even help healthcare providers [detect life-threatening conditions](/en/success-stories/hca-healthcare). Many of us benefit from and interact with machine learning on a daily basis. Some common machine learning uses include: * Recommendation algorithms on your favorite streaming services. * Automatic helplines and chatbots. * Targeted ads. * Automated quotes from financial institutions. [Compare predictive AI vs. generative AI](/en/topics/ai/predictive-ai-vs-generative-ai) [Generative AI](/en/topics/ai/what-is-generative-ai), which now powers many AI tools, is made possible through deep learning, a machine learning technique for analyzing and interpreting large amounts of data. [Large language models (LLMs),](/en/topics/ai/what-are-large-language-models) a subset of generative AI, represent a crucial application of machine learning by demonstrating the capacity to understand and generate human language at an unprecedented scale. Machine learning is becoming an expected feature for many companies to use, and transformative [AI/ML use cases](/en/topics/ai/ai-ml-use-cases) are occurring across healthcare, financial services, telecommunications, [government](/en/success-stories/capgemini), and other industries. [Explore generative AI use cases](/en/products/ai/generative-ai) [Explore predictive AI use cases](/en/products/ai/predictive-ai) Challenges and considerations for machine learning models --------------------------------------------------------- **ML models can be biased:** Machine learning models learn from patterns in historical data, which means they can learn **bias and discrimination**. For example, data can reflect existing racial, gender-based, or socioeconomic biases in society. If training data is not scrubbed for bias, the model can perpetuate and amplify those biases. Likewise, decisions made by machine learning models, such as loan approvals, hiring, or criminal sentencing, can disproportionately affect marginalized groups. Fairness frameworks exist to ensure equitable outcomes across different groups. **ML models may not show their work:** Some machine learning models are "**black boxes**," meaning their internal processes are not understood. This lack of transparency makes it difficult for humans to understand how a model makes a decision, which can lead to a lack of trust. To solve for this, [explainable AI (XAI)](/en/topics/ai/what-explainable-ai) techniques can be applied to make AI outputs more understandable to humans. Implementing XAI requires committing to more transparency throughout the entire machine learning lifecycle—from initial design to monitoring. [Learn more about explainable AI](/en/topics/ai/what-explainable-ai) How Red Hat can help -------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Enterprise](/en/products/ai/enterprise), a platform for deploying, managing, and scaling AI inference, agentic AI workflows, and AI-powered applications on any infrastructure. [Explore Red Hat AI](/en/products/ai) The official Red Hat blog ------------------------- Get the latest information about our ecosystem of customers, partners, and communities. [Keep reading](/en/blog "The official Red Hat blog") All Red Hat product trials -------------------------- Our no-cost product trials help you gain hands-on experience, prepare for a certification, or assess if a product is right for your organization. [Keep reading](/en/products/trials "All Red Hat product trials") Keep reading ------------ ### What is agentic AI? Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. [Read the article](/en/topics/ai/what-is-agentic-ai "article | What is agentic AI?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") ### What are large language models? A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language. [Read the article](/en/topics/ai/what-are-large-language-models "article | What are large language models?") 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