What Is Machine Learning?
Machine learning is a type of artificial intelligence that performs data analysis tasks without explicit instructions. Machine learning technology can process large quantities of historical data, identify patterns, and predict new relationships between previously unknown data.What Is Machine Learning?
Machine learning is a type of artificial intelligence that performs data analysis tasks without explicit instructions. Machine learning technology can process large quantities of historical data, identify patterns, and predict new relationships between previously unknown data. You can perform classification and prediction tasks on documents, images, numbers, and other data types.
For example, a financial organization could train a machine learning system to classify fraudulent and genuine transactions. The system identifies patterns in known data to accurately guess or predict whether a new transaction is genuine.
Machine learning benefits
Data is the critical driving force behind business decision-making. Modern organizations generate data from thousands of sources, including smart sensors, customer portals, social media, and application logs. Machine learning automates and optimizes the process of data collection, classification, and analysis. Businesses can drive growth, unlock new revenue streams, and solve challenging problems faster.
What is the difference between machine learning and artificial intelligence?
While the terms machine learning and artificial intelligence (AI) are used interchangeably, they are not the same. Machine learning is one of many branches of AI. While machine learning is AI, not all AI activities can be called machine learning.
Artificial intelligence is an umbrella term for different strategies and techniques used to make machines more human-like. AI includes everything from smart assistants like Alexa, chatbots, and image generators to robotic vacuum cleaners and self-driving cars.
In contrast, machine learning models perform more specific data analysis tasks—like classifying documents, labeling images, or predicting the maintenance schedule of factory equipment. Machine learning technology is primarily based on mathematics and statistics, while other types of AI are more complex.
Learn more about machine learning vs. artificial intelligence
Machine learning use cases and real-world examples
Let’s take a look at machine learning applications in some key industries.
Manufacturing
Machine learning can support predictive maintenance, quality control, and innovative research in the manufacturing sector. It also helps companies improve logistical solutions, including assets, supply chain, and inventory management. For example, manufacturing giant 3M uses machine learning to innovate sandpaper. Machine learning algorithms enable 3M researchers to analyze how slight changes in shape, size, and orientation improve abrasiveness and durability. Those suggestions inform the manufacturing process.
Healthcare and life sciences
The proliferation of wearable sensors and devices has generated significant health data. Machine learning programs analyze this information and support doctors in real-time diagnosis and treatment. Machine learning researchers are developing solutions that detect cancerous tumors and diagnose eye diseases, significantly impacting human health outcomes. For example, Cambia Health Solutions uses machine learning to automate and customize treatment for pregnant women.
Financial services
Financial machine learning projects improve risk analytics and regulation. Machine learning technology allows investors to identify new opportunities by analyzing stock market movements, evaluating hedge funds, or calibrating financial portfolios. In addition, it can help identify high-risk loan clients and mitigate signs of fraud. For example, NerdWallet, a personal finance company, uses machine learning to compare financial products like credit cards, banking, and loans.
Retail
Retail can use machine learning to improve customer service, stock management, upselling, and cross-channel marketing. For example, Amazon Fulfillment (AFT) cut infrastructure costs by 40 percent using a machine learning model to identify misplaced inventory. This helps them deliver on Amazon’s promise that an item will be readily available to customers and arrive on time despite processing millions of global shipments annually.
Media and entertainment
Entertainment companies turn to machine learning to better understand their target audiences and deliver immersive, personalized, and on-demand content. Machine learning algorithms are deployed to help design trailers and other advertisements, provide consumers with personalized content recommendations, and even streamline production.
For example, Disney uses machine learning to archive its media library. Machine learning tools automatically tag, describe, and sort media content, enabling Disney writers and animators to quickly search for and familiarize themselves with Disney characters.
How does machine learning work?
The central idea behind machine learning is an existing mathematical relationship between any input and output data combination. The machine learning model does not know this relationship in advance but can guess if sufficient examples of input-output data sets are given. This means every machine learning algorithm is built around a modifiable math function. The underlying principle can be understood like this: We ‘train’ the algorithm by giving it the following input/output (i,o) combinations – (2,10), (5,19), and (9,31) The algorithm computes the relationship between input and output to be: o=3*i+4 We then give it input 7 and ask it to predict the output. It can automatically determine the output as 25. While this is a basic understanding, machine learning focuses on the principle that computer systems can mathematically link all complex data points as long as they have sufficient data and computing power to process. Therefore, the accuracy of the output is directly co-relational to the magnitude of the input given.
Are machine learning models deterministic?
If a system’s output is predictable, then it is said to be deterministic. Most software applications respond predictably to the user's action, so you can say: “If the user does this, he gets that.” However, machine learning algorithms learn through observation along with experiences. Therefore, they are probabilistic in nature. The statement now changes to: “If the user does this, there is an X% chance of that happening.”
In machine learning, determinism is a strategy used while applying the learning methods described above. Any of the supervised, unsupervised, and other training methods can be made deterministic depending on the business's desired outcomes. The research question, data retrieval, structure, and storage decisions determine if a deterministic or non-deterministic strategy is adopted.
Deterministic vs. probabilistic approach
The deterministic approach focuses on the accuracy and the amount of data collected, so efficiency is prioritized over uncertainty. On the other hand, the non-deterministic (or probabilistic) process is designed to manage the chance factor. Built-in tools are integrated into machine learning algorithms to help quantify, identify, and measure uncertainty during learning and observation.
Machine learning training for beginners
Machine learning requires a strong foundation in mathematics, statistics, coding, and data technologies. Those wishing to advance in machine learning should consider completing a master's degree in artificial intelligence or data science. These programs typically involve topics such as neural networks, natural language processing, and computer vision in-depth.
However, formal education isn’t the only path. You can use online courses to learn at your own pace and master specific skills. Machine learning training on AWS includes certifications by AWS experts on topics like:
How can you implement machine learning in your organization?
Getting started with machine learning requires implementing the machine learning lifecycle. It contains the following phases.
Business goal
An organization considering machine learning should first identify the problems it wants to solve. Identify the business value you gain by using machine learning in problem-solving. Can you measure the business value using specific success criteria for business objectives? A goal-oriented approach helps you justify expenditures and convince key stakeholders.
Problem framing
Next, frame the business problem as a machine learning problem. Identify what is observed and what should be predicted. A key step in this phase is to determine what to predict and how to optimize related performance and error metrics.
Data processing
Data processing converts data into a usable format using machine learning algorithms. It includes identifying, collecting, and preprocessing data along with feature engineering. You create, transform, extract, and select machine-learning variables from your data.
Model development and deployment
This is the core process of training, tuning, and evaluating your model, as described in the previous section. It includes establishing MLOps. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. They unify ML development with deployment and operations. For example, you create a CI/CD pipeline that automates the build, train, and release to staging and production environments.
Monitoring
A model monitoring system ensures your model maintains a desired performance level through early detection and mitigation. It includes collecting user feedback to maintain and improve the model so it remains relevant over time.
What are the challenges in machine learning implementation?
View the challenges in machine learning implementation
How can AWS machine learning help?
AWS puts machine learning in the hands of every developer, data scientist, and business user. AWS Machine Learning services provide high-performing, cost-effective, and scalable infrastructure to meet business needs.