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Machine learning (ML) is a key component of artificial intelligence that allows decision systems to improve by learning from data rather than being explicitly programmed for each task.
This meta-awareness is the root idea behind calling AI intelligent — it simulates the output of various human cognitive processes.
Therefore, AI is best seen as an augmentation or accelerant of human intelligence, and ML provides the theoretical and mathematical infrastructure. Here, we take a comprehensive look at machine learning technology.
Machine Learning Explained
- What is Machine Learning?
- How Does Machine Learning Work?
- What Are the Features of Machine Learning?
- How Important is Machine Learning?
- What Are Machine Learning Use Cases?
- What Are Machine Learning Production Rates?
- Machine Learning Companies
What is Machine Learning?
Machine learning is a branch of Artificial Intelligence in which systems learn from data rather than following strict rules programmed by humans as in traditional software.
With machine learning, instead of every action being dictated by code, ML systems recognize patterns in data and make decisions or predictions based on their findings. The patterns that ML systems identify are often unknown to their developers, making machine learning a useful tool for strategic and adaptable problem solving.
Various ML algorithms have been around since the 1800s, but Arthur Samuel formalized the idea of machine learning in 1959.
Samuel, an IBM researcher, developed the first machine learning approach to teach a machine to play checkers and improve over time. This was a revolutionary and interactive application of existing mathematical models. Around the same time, Frank Rosenblatt developed the “Perceptron” at Cornell University, which was the first application of a neural network, which is a model based on the structure of neurons in the human brain and underpins modern deep learning.
Machine Learning Vs. Deep Learning
Deep learning is a subset of machine learning that uses multi-layer neural network models to analyze complex and high-dimensional data sets.
This makes deep learning particularly suited for complex and subjective tasks, such as image recognition, natural language processing (NLP) and autonomous driving. Deep learning can also process these larger data sets with less human intervention than traditional ML, making it a more scalable yet complicated solution, according to IBM.
Not all machine learning is deep learning, but all deep learning is ML.
Machine Learning in Modern Software
Traditional mathematical models and computer software have rigid and predefined rules for specific tasks. ML allows pattern recognition and actions beyond a human’s ability to program themselves.This scaling impact amplifies over time, as new information provides new insights and signals for the model — much like a human learns from new experiences.
For example, a sales team using machine learning could automatically optimize its outreach strategy based on past interactions or adjust pricing, improving conversions without constant manual salesperson adjustments, according to SetSail.
Various training approaches help develop machine learning and deep learning models, including:
- Supervised learning: The system learns from labeled examples, such as identifying fraudulent transactions in finance.
- Unsupervised learning: The system finds patterns in unlabeled data, such as customer segmentation in retail.
- Reinforcement learning: The system improves through trial and error based on feedback, such as optimizing chatbot responses.
Each training approach suits different applications, such fraud detection with supervised learning and identifying customer behavior patterns with unsupervised learning.
This video by IBM offers an additional explanation of machine learning technology:
How Does Machine Learning Work?
One of the core objectives from the beginning of machine learning is to try to emulate the way the human brain operates.
Similar to educational techniques, the process of developing and using machine learning models typically follows a consistent workflow across specific approaches:
- Data collection: Relevant data — such as sales figures, customer behavior logs or operational metrics — are gathered.
- Model building: Data is fed into an algorithm, which learns from the data and identifies patterns to develop a mathematical model.
- Training: The model is trained on a portion of the data to ensure accuracy, refining its predictions or decisions over time.
- Evaluation: The model’s performance is tested on new, unseen data to confirm its reliability.
- Deployment: Once the model performs well, it’s integrated into software for use in real-world scenarios.
- Inference: The model continues to process live data, improving as it receives new inputs.
This cycle can be automated, allowing companies to focus on strategy while the system continuously improves without direct oversight.
Core Technologies Used for AI
Building, training and deploying machine learning models requires the effective integration of a wide range of technologies and software approaches. Key software and technology stack considerations include:
- Data storage and management: Machine learning requires vast amounts of data, which is stored in data warehouses or data lakes, according to Microsoft. Core technologies include SQL (for structured data), NoSQL (for unstructured or semi-structured data) and cloud storage solutions, such as AWS, Azure and Google Cloud.
- Data preprocessing tools: Data must be cleaned, transformed and standardized before generating a model. Tools like Pandas and NumPy (for Python) handle data manipulation and analysis, while extract, transform and load (ETL) pipelines can automate the process of converting raw data into a suitable format for ML models, according to AWS.
- Relevant programming languages: Python is the most widely used language due to its simplicity and support, according to Turing. R is better for statistical modeling, while alternatives offer performance advantages in certain high-computation or production environments, according to IBM.
- Machine learning frameworks: ML frameworks streamline the process of building, training and deploying models. Scikit-learn is favored for traditional ML algorithms, like classification and regression, while TensorFlow and PyTorch are popular for deep learning.
- Hardware and computational resources: Training machine learning models, especially deep learning models, requires significant computational power or compute. Having sufficient access to compute in the form of GPU chips, such as NVIDIA’s GeForce RTX 4070 Super, or TPUs developed by Google, is an essential part of advanced ML and deep learning.
Other technologies that are also critical parts of a machine learning deployment plan include model optimization tools (to fine-tune model performance), deployment tools (to provide real-time access) and monitoring tools (to track accuracy and uptime).
This is a helpful tutorial by Google describing a seven-step approach to building a machine learning system:See more: 10 Top Machine Learning Certifications
What Are the Features of Machine Learning?
Machine learning offers several technology features that can revolutionize use cases across an organization's operations. It can automate tasks, contextualize insights and can enhance customer experience (CX).
Below are key features and real-world examples of machine learning in practice.
Automation
Machine learning-driven systems can automate repetitive tasks, such as data entry and analysis, significantly reducing manual workloads. This frees work time for more strategic actions. For example:
- A customer relationship management (CRM) platform may automatically clean up a customer profile, helping sales teams rapidly prioritize a new customer list.
- Image editing software may automatically enhance specific parts of a photo, such as removing red-eye.
Prediction
Machine learning excels at forecasting trends by analyzing historical data. This provides data-driven decision-making insights to inform high-level strategies and specific ground-level actions. For example:
- A customer support and ticketing system may identify customers who are likely to churn or those likely to be interested in a new product.
- A manufacturing asset tracking system can forecast equipment failures to avoid costly losses.
Personalization
Machine learning enables personalized experiences by tailoring content based on user preferences and behavior. Examples of this include:
- An e-commerce website may provide a flash discount to certain customers who are discount hunters.
- A content streaming service will show certain movies and shows to users who are most likely to watch them.
Continuous Improvement
One of machine learning’s core strengths is its ability to improve through learning over time. The more data models consume the more opportunity to improve accuracy and effectiveness. For example:
- Driving navigation systems may update directions based on a sudden traffic slow down.
- Spam-detection email systems may identify and flag new attempts by scammers and phishers to avoid detection.
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Each of these features can increase a company’s ability to adapt to market forces. They often provide operational benefits as well, freeing up resources to focus on new or more complex challenges outside the scope of machine learning.
However, as machine learning techniques continue to advance, ML will likely impact or disrupt nearly all occupations, according to a study by MIT. This will require strong leadership and transformational “job redesigns” across most professional roles and organizations.
How Important is Machine Learning?
Machine learning is no longer a niche technology.
The technology is becoming essential to transform and evolve digitally, and there’s truly a need for it: Most companies struggle to see expected revenue gains from their digitization efforts, even as more commerce occurs digitally, according to McKinsey and Company.
Benefits of Machine Learning
Developing a practice of machine learning provides an opportunity to unlock insights that inform leadership strategy, such as:
- Data-driven decision making: Machine learning allows companies to quickly process massive amounts of data, helping identify patterns, spot trends and uncover new opportunities that might otherwise be missed. For example, Deliveroo uses ML across its business to coordinate its global teams, according to an AWS case study.
- Operational efficiency: By automating repetitive tasks and analyzing data at scale, machine learning boosts efficiency across multiple functions, from supply chain optimization to customer support. In one instance, ML techniques were able to effectively process the most important information from 70% of claims documents, according to an EY case study.
- Enhanced customer experience: Machine learning personalizes customer interactions, such as through recommendation engines and chatbots offering real-time support. This improves customer satisfaction and loyalty by delivering more relevant and timely interactions, while reducing operational overhead. For example, the Development Bank of Singapore reduced its overhead by 20% using ML, while maintaining its 100% customer satisfaction scores, according to Tearsheet.
By adopting machine learning, companies can make more informed decisions, streamline operations and deliver superior customer experiences — all contributing to long-term success and market competitiveness.
Challenges of Machine Learning
While machine learning provides numerous opportunities for operational improvements and efficiencies, it's a technology that also presents challenges for both leaders and technicians.
One of the main hurdles is the quality of training data. “Garbage In, Garbage Out” is a computer science and machine learning mantra.
If the data used to train the model contains errors or biases — such as incomplete or skewed data sets — those biases can be embedded into a model, leading to inaccurate predictions.
Furthermore, “black-box” models can be opaque, making it difficult to identify errors. Over-reliance on an inaccurate model can create significant problems, especially in hiring, security and credit scoring. Effective and ongoing training and implementation are essential to avoid pitfalls.
What Are Machine Learning Use Cases?
Machine learning applications span across industries, transforming business outcomes across various functions. Here are some examples of how machine learning integration can create value across verticals, with each of the examples being a common use case:
- Manufacturing: Assembly line optimization is a key area of use, and predictive maintenance helps manufacturers anticipate when equipment will fail. Both initiatives reduce downtime and repair costs.
- Finance: Fraud detection systems analyze transaction patterns to flag unusual activities, reducing financial risk and protecting assets. Compliance and payment processing are expected to be areas of growth.
- Retail: Customer engagement and activation tools that analyze purchase history and then encourage repeat or new behavior. Demand prediction and supply chain optimization are growing areas of interest.
- Health care: Patient monitoring and physician scheduling systems make complicated decisions and reduce operational complexity. ML-assisted health is a consistent goal within health care and is considered one of the key opportunities across all ML applications.
As with any tool, machine learning is best used in the context of trying to solve a specific problem.
Foundational considerations should always be around the availability of clean data and the need to explain the outputs of a model. Here are some of the most common considerations for identifying if machine learning fits a particular use case:
- Categorization: You have a set of data that you want to classify into specific areas (e.g. fraudulent, valid, satisfied, dissatisfied)
- Complex rules: Use ML when traditional rule-based systems can’t handle too many variables (e.g. complex IF-THEN labeling)
- Clustering: You want to directionally group data without predefined labels (e.g. customer segmentation)
- Complexity reduction: You want to simplify large data sets to focus on key information (e.g. image or video analysis)
- Prediction: Forecast values based on historical data (e.g., stock prices, equipment failure)
- Rapid scalability: Use ML when you need rapid decision-making for large-scale tasks (e.g., processing millions of emails)
- Available resources and capabilities: A biased and inaccurate ML model can do more harm than good, so only use ML that matches your internal management capabilities.
This video by IBM discusses additional machine learning use cases:
See more: 5 Machine Learning Case Studies
What Are Machine Learning Production Rates?
Despite the availability of user-friendly machine learning tools, many initiatives fail due to leadership — not software — gaps.
Only 32% of machine learning projects successfully move from pilot to production, according to a 2023 report by Rexer Analytics.
The report cites poor goal setting and weak leadership as leading causes. Even when machine learning initiatives are successful, they can still take a substantial investment of time and resources: 20% of ML initiatives take three to six months to deploy, according to a 2024 study by The Institute for Ethical AI & Machine Learning.
To ensure success, business leaders need to:
- Establish clear and measurable goals for machine learning initiatives with enough time and resources to be successful
- Foster cross-functional collaboration between data scientists, engineers, and decision-makers
- Provide training and upskilling, so teams can fully understand and leverage machine learning outputs
Strong leadership is crucial to unlocking machine learning’s potential and driving meaningful business outcomes.
Machine Learning Companies
Machine learning is a compute- and energy-intensive task, generally requiring the use of cloud infrastructure and data centers. As a result, AWS, Microsoft Azure and Google Cloud are key infrastructure companies, providing general platforms to launch machine learning initiatives.
Beyond the tech giants, other notable machine learning companies include:
- Databricks: Data warehouses and data lakes for an open and unified platform for data and AI
- Dataiku: AutoML provides builders of ML models with automatic feature generation
- Datarobot: ML platform that enables users to build and deploy ML models
- H2O.ai: GenAI platform where customers own every part of the stack
- Hugging Face: Open-source platform for community models, data sets and applications
- IBM: Watson Machine Learning offers ML products
- Mathworks: MATLAB helps users engineer features from data and fit ML models
- NVIDIA: Makes graphics processing units (GPUs) that provide the compute power to handle ML loads
- SAS: Viya supports data mining and ML processes with visual and programming interfaces
- Snowflake: Connects companies globally across data to productize AI and applications
Other notable organizations include the Python community, OpenML and Kaggle, which provide community discussion and free data sets for ML projects.
See more: 10 Top Machine Learning Companies
