Machine learning (ML) and artificial intelligence (AI) are often used interchangeably in tech discussions, yet they represent distinct concepts with important differences.

While AI refers to the broader field of creating machines capable of intelligent behavior that mimics human capabilities, machine learning is a specific subset of AI focused on developing algorithms that allow computers to learn from and make predictions based on data.

Understanding the distinction between these technologies is crucial for businesses and developers looking to implement these solutions effectively, as each offers unique approaches, capabilities, and limitations.

What’s the difference between AI and machine learning?

AI represents the broader discipline focused on creating systems that can perform tasks requiring human-like intelligence. These systems range from rule-based programs that follow specific instructions to sophisticated frameworks that can reason, perceive, and adapt to new situations.

AI encompasses technologies that enable computers to simulate human cognitive functions across various domains—from understanding natural language to making complex decisions based on incomplete information.

Machine learning takes a more specialized approach by developing algorithms that improve through experience rather than explicit programming. ML systems analyze data to identify patterns, build predictive models, and refine their accuracy over time without human intervention.

The fundamental distinction is that AI represents the comprehensive goal of machine intelligence, while ML provides a specific, data-driven methodology to achieve aspects of that intelligence.

Understanding the differences between AI and machine learning enables DevOps teams to make intelligent decisions about how to utilize tools and create workflows. Concepts like MLOps and LLMOps provide an advantage for teams by offering a roadmap for using AI and ML tools efficiently.

Use cases for AI

Some common uses for AI include:

• Intelligent document processing automates the extraction of critical information from contracts, invoices, and insurance forms.
• Clinical decision support systems help radiologists identify subtle anomalies in medical imaging with greater accuracy.
• Conversational AI platforms power enterprise customer service, handling routine inquiries without human intervention.
• Autonomous warehouse robots navigate fulfillment centers to retrieve and transport inventory in logistics operations.
• AI-powered legal research tools analyze case documents and precedents to accelerate legal discovery processes.

Use cases for machine learning

Organizations often employ machine learning for:

• Credit risk models analyze thousands of variables to make lending decisions in milliseconds for financial institutions.
• Predictive maintenance systems monitor industrial equipment vibration patterns to prevent costly manufacturing downtime.
• Recommendation engines analyze viewing behavior to personalize content suggestions for streaming service subscribers.
• Computer vision systems identify weeds in agricultural fields for targeted herbicide application.
• Natural language processing models analyze clinical notes to improve medical coding accuracy in healthcare systems.

How AI works

Modern AI systems, particularly those behind recent breakthroughs in language and image understanding, rely heavily on deep learning architectures and vast computational resources. Large Language Models (LLMs) like GPT-4, Claude, and LLaMA represent the cutting edge of these approaches, using transformer neural networks with billions or even trillions of parameters.

These systems learn by processing enormous datasets—often comprising hundreds of billions of words from books, articles, and websites—to identify complex patterns in language. The transformer architecture allows these models to weigh the importance of different words in context, enabling them to generate coherent text, answer questions, and even reason through problems.

Beyond language models, AI encompasses diverse technologies addressing different aspects of intelligence:

• Computer vision systems using convolutional neural networks enable machines to recognize objects, interpret scenes, and track movement in images and video.
• Reinforcement learning powers systems that learn optimal strategies through trial and error, driving advances in robotics and game-playing AI like AlphaGo.
• Multimodal models combine text, image, audio, and sometimes video understanding within unified frameworks, allowing for more sophisticated interactions with the world.

Meanwhile, symbolic and hybrid AI approaches continue to evolve, integrating neural networks with explicit reasoning mechanisms to overcome the limitations of purely statistical methods. These diverse branches of AI complement each other, addressing different cognitive capabilities while collectively pushing the boundaries of what machines can accomplish.

How machine learning works

Machine learning operates on the fundamental principle that systems can learn from data without explicit programming.

The process begins with gathering relevant datasets, which are then cleaned and prepared for analysis. These datasets serve as the foundation for training ML models to recognize patterns and make predictions. Depending on the problem, developers select appropriate algorithms—such as linear regression for predicting numerical values, decision trees for classification tasks, or clustering algorithms for grouping similar items.

During training, the algorithm analyzes the data, identifies patterns, and builds a mathematical model that captures relationships between inputs and outputs. This model is then tested on new data to evaluate its accuracy and further refined through iterative processes like hyperparameter tuning and cross-validation.

Machine learning operates through three main approaches:

• Supervised learning
• Unsupervised learning
• Reinforcement learning

In supervised learning, models train on labeled data where the desired outputs are provided, allowing the system to learn mappings between inputs and correct answers—this approach powers applications like email spam detection and medical diagnosis.

Unsupervised learning works with unlabeled data, identifying hidden structures and relationships without predefined categories—enabling customer segmentation and anomaly detection in cybersecurity.

Finally, reinforcement learning trains agents through reward signals as they interact with environments, learning optimal strategies through trial and error—driving advances in game-playing AI and robotic control systems.

How machine learning and AI work together

Rather than functioning as separate domains, machine learning and AI often work together to create powerful systems that tackle complex problems. ML identifies patterns in data efficiently, while AI provides the reasoning frameworks and knowledge structures needed for complex decision-making.

Today’s most effective systems combine both approaches: ML components handle pattern recognition and predictions, while rule-based systems manage logical operations and explicit knowledge. This partnership solves problems neither could address alone.

Consider autonomous vehicles: Machine learning analyzes sensor data to identify objects and predict movement patterns, while symbolic AI manages navigation decisions and follows traffic regulations.

Similarly, LLMs demonstrate this integration at scale. These models use ML to develop sophisticated language capabilities by analyzing patterns in massive text datasets. AI systems then deploy these capabilities in practical applications like customer service chatbots, content generation, and search engines.

This collaboration continues to evolve through approaches like neuro-symbolic AI, which combines neural networks’ pattern recognition with symbolic reasoning’s precision to create more trustworthy and capable systems.

How CI/CD supports AI and machine learning

CI/CD refers to the practice of automatically integrating code changes from multiple contributors into a shared repository, then automating the building, testing, and deployment of those changes. CI/CD creates a reliable pipeline that transforms raw code into production-ready software through automated processes rather than manual steps.

For AI and ML specifically, CI/CD addresses unique challenges posed by their experimental nature. Models undergo constant refinement—parameters adjusted, architectures modified, training approaches rethought. A well-designed CI/CD pipeline automatically validates these changes, preserving system integrity while shortening iteration cycles from days to hours.

CI/CD also improves collaboration across AI and ML development teams. Data scientists, ML engineers, and software developers operate with different priorities and workflows. CI/CD bridges these differences by establishing a common validation framework. Code changes undergo consistent quality checks before integration, preventing conflicts and maintaining codebase stability.

Consider a practical example: A data scientist develops an improved feature extraction technique. Instead of manual integration that might break existing systems, the CI/CD pipeline verifies compatibility with production infrastructure before merging. This automated verification creates confidence without sacrificing momentum—a critical balance in AI development.

Testing AI and machine learning applications

Testing AI and machine learning systems presents unique challenges that CI/CD helps address. Unlike traditional software that produces deterministic outputs, ML models generate probabilistic results that may vary even with identical inputs. This makes testing AI and ML features more difficult than traditional software.

Effective testing must evaluate model performance across statistical distributions rather than exact outputs. CI/CD pipelines can be configured to automate these specialized tests—measuring metrics like accuracy, precision, recall, and bias across diverse datasets—on every code change.

The pipeline can also implement data validation tests to catch data drift or poisoning, model validation to prevent performance regression, and A/B testing frameworks to compare model versions under controlled conditions. CI/CD pipelines can even be used to prevent LLM hallucinations.

Beyond testing, CI/CD supports the entire ML workflow from data preparation to deployment. Pipelines can automate dataset versioning, feature engineering, model training, hyperparameter optimization, and model packaging.

This end-to-end automation ensures reproducibility—a critical requirement for scientific rigor and regulatory compliance in AI applications. By maintaining consistent environments across development, testing, and production stages, CI/CD eliminates the “it works on my machine” problem that often plagues complex AI deployments, ensuring that models behave consistently across environments.

Check out a practical demonstration of testing LLM apps with LangChain and CircleCI.

Conclusion

Machine learning and artificial intelligence represent different yet complementary approaches to creating intelligent systems. While AI encompasses the broader goal of building machines capable of human-like reasoning across diverse tasks, machine learning provides the data-driven methodology that powers many modern AI capabilities through pattern recognition and statistical learning.

For teams developing these technologies, CI/CD pipelines offer critical infrastructure that improves collaboration, automates testing, ensures model reproducibility, and accelerates the experimental cycle—addressing the unique challenges of AI development like probabilistic testing, data validation, and environment consistency.

Experience how these CI/CD capabilities can transform your AI and ML development workflows by signing up for a free CircleCI account today.