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What is machine learning?

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Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed.

Machine learning (ML) is a subcategory of artificial intelligence (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.

Artificial intelligence happens 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. 

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 styles:

Supervised learning models 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.

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. Many of us benefit from and interact with machine learning on a daily basis. Some common examples include:

  • Recommendation algorithms on your favorite streaming services.
  • Automatic helplines and chatbots.
  • Targeted ads.
  • Automated quotes from financial institutions.

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), 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 are occurring across healthcare, financial services, telecommunications, government, and other industries.

Red Hat has partnered with IBM to create Ansible® Lightspeed with IBM watsonx Code Assistant—a generative AI service that helps developers create Ansible content more efficiently.

Red Hat provides the common foundations for your teams to build and deploy AI applications and machine learning (ML) models with transparency and control. 

Red Hat® OpenShift® AI is a platform that can train, prompt-tune, fine tune, and serve AI models for your unique use case and with your own data.

For large AI deployments, Red Hat OpenShift offers a scalable application platform suitable for AI workloads, complete with access to popular hardware accelerators.

Red Hat is also using our own Red Hat OpenShift AI tools to improve the utility of other open source software, starting with Red Hat Ansible® Lightspeed with IBM watsonx Code Assistant. Ansible Lightspeed helps developers create Ansible content more efficiently. It reads plain English entered by a user, and then it interacts with IBM watsonx foundation models to generate code recommendations for automation tasks that are then used to create Ansible Playbooks.

Additionally, Red Hat’s partner integrations open the doors to an ecosystem of trusted AI tools built to work with open source platforms.



InstructLab is an open source project for enhancing large language models (LLMs).

Keep reading


What is generative AI?

Generative AI relies on deep learning models trained on large data sets to create new content.


What is machine learning?

Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed.


What are foundation models?

A foundation model is a type of machine learning (ML) model that is pre-trained to perform a range of tasks. 

More about AI/ML



A foundation model platform used to seamlessly develop, test, and run Granite family LLMs for enterprise applications.

An AI-focused portfolio that provides tools to train, tune, serve, monitor, and manage AI/ML experiments and models on Red Hat OpenShift.

An enterprise application platform with a unified set of tested services for bringing apps to market on your choice of infrastructure. 

Red Hat Ansible Lightspeed with IBM watsonx Code Assistant is a generative AI service designed by and for Ansible automators, operators, and developers. 



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