Machine Learning (ML) is a core branch of artificial intelligence (AI) that enables systems to automatically learn patterns from data and use them to make predictions or decisions. By training and iteratively refining algorithm models on vast amounts of data, ML enables enterprises to unlock the value of their data, transition from rigid rule-based operations to agile data-driven strategies, and significantly enhance both automation and decision-making efficiency.

In the era of digital transformation, enterprises have accumulated massive volumes of business data. However, traditional data processing methods—relying on fixed rules and manual expertise—no longer suffice to address complex, ever-changing business requirements. Manual rule extraction is not only inefficient but also struggles to maintain accuracy and real-time responsiveness when handling high-dimensional, non-linear, and rapidly changing data features. Consequently, valuable data assets remain underutilized, creating bottlenecks for business growth. Addressing how to efficiently extract value from this data to achieve intelligent operations has become an urgent priority for organizations.

Machine learning addresses these challenges by using algorithms to automatically learn patterns from data, processing complex tasks without the need for explicit programming. These systems can adapt to environmental changes and continuously optimize model performance, delivering efficient and accurate solutions in critical scenarios such as precision marketing, risk control, and intelligent O&M. By adopting ML, enterprises can seize the opportunities of intelligent transformation, reduce operational costs, improve efficiency, and drive service innovation.

• Automatic decision-making: Machine learning models automatically identify patterns in historical data to execute prediction or classification tasks. This automation reduces manual intervention, thereby improving both the efficiency and consistency of decision-making processes. These capabilities are particularly valuable in high-frequency, large-scale service scenarios.

• Self-adaptation and continuous optimization: As new data becomes available, models can be continuously refined through online learning or periodic retraining. This iterative process allows models to adapt to evolving service environments, ensuring sustained high accuracy and robustness over time.

• Processing of high-dimensional complex data: Unlike traditional statistical methods, ML algorithms excel at handling unstructured data—such as images, text, and audio—as well as high-dimensional feature spaces. By mining deep, non-linear associations within this complex data, ML expands the boundaries of what is possible with data applications.

• Large-scale scalability: Leveraging the elastic computing resources of cloud computing platforms, ML tasks can be seamlessly deployed for distributed training and inference. This scalability ensures that ML systems can efficiently handle varying workloads, from initial experiment verification to supporting concurrent access by millions of users.

• Financial risk control

  • Target Users: Financial institutions, banks, and payment platforms.

  • Core Challenges: Transaction data is massive and requires real-time processing. Fraud tactics evolve rapidly, rendering traditional rule-based engines ineffective due to poor coverage of new risks and high false-positive rates.

  • Specific Tasks: Develop anti-fraud models that analyze transaction behavioral features in real time to identify anomalous patterns and assess credit risks.

  • Desired Outcome: Fraud-related losses are significantly reduced. Accuracy and speed in risk identification are enhanced, leading to improved user experience and guaranteed fund security.

• Intelligent recommendation

  • Target Users: E-commerce platforms, content information platforms, and video streaming services.

  • Core Challenges: Information overload hinders users from quickly discovering relevant content. Platforms face pressure to increase user retention and conversion rates.

  • Specific Tasks: Implement collaborative filtering or deep learning models to generate personalized recommendation lists. These models leverage user historical behavior, explicit preferences, and contextual information.

  • Desired Outcome: The click-through rate (CTR) and purchase conversion rate are increased. User loyalty and satisfaction are improved, enabling more refined operations.

• Industrial predictive maintenance

  • Target Users: Manufacturing enterprises, energy sector operators, and equipment O&M teams.

  • Core Challenges: Downtime for critical assets incurs high costs. Traditional periodic maintenance is often inefficient, leading to either excessive upkeep or insufficient attention. There is a lack of accurate prediction regarding device health status.

  • Specific Tasks: Aggregate time-series data from sensors to build fault prediction models. Continuously monitor equipment status and trigger early warnings for potential failures.

  • Desired Outcome: The unplanned downtime is reduced. The maintenance strategy and spare parts management are optimized. The equipment lifespan is extended. The overall O&M costs are reduced.

• Natural language processing (NLP)

  • Target Users: Customer service centers, research institutes, and document-intensive enterprises.

  • Core Challenges: Manual processing of high-volume text inquiries or documents is inefficient. Human emotional variability can impact service quality, and efficient knowledge retrieval remains difficult.

  • Specific Tasks: Deploy intelligent customer service chatbots, sentiment analysis models, or automated document summarization systems to understand and generate human language automatically.

  • Desired Outcome: ML delivers faster response times and broader coverage in customer service. It frees up human agents to focus on high-value tasks and accelerates knowledge transfer and utilization.

The origin of machine learning traces back to the 1950s. Initially, the field aimed to simulate human cognitive processes and solve logical reasoning and basic pattern recognition problems. In the early stage, research primarily focused on linear models, such as perceptrons. However, progress was constrained by limited compute and data scarcity, which restricted the processing capabilities of these early models.

From the 1980s to the 1990s, advanced significantly with the introduction of backpropagation algorithms and the maturation of statistical learning theories, including Support Vector Machines (SVM) and decision trees. This era saw substantial improvements in feature engineering, leading to widespread adoption in applications like handwriting recognition. Nevertheless, the reliance on manual feature extraction remained a bottleneck, limiting the effectiveness of these models in complex scenarios.

In the 21st century, the explosive growth of the Internet generated massive datasets, while breakthroughs in GPU compute catalyzed the rise of deep learning. Deep neural networks demonstrated the ability to automatically extract high-order features, achieving significant breakthroughs in image and speech recognition. This shift propelled machine learning from academic laboratories into large-scale industrial applications.

Currently, the field is evolving toward the integration of large-scale models with cloud-native architectures. Pre-trained large models exhibit powerful generalization capabilities. When combined with the elastic computing resources and automated operations (MLOps) provided by cloud platforms, these models streamline development, deployment, and management. This synergy is driving intelligent upgrades across various industries by making advanced AI more efficient and accessible.

A machine learning (ML) system consists of four core modules: data processing, model training, model evaluation and optimization, and service deployment. These components operate in concert to establish a closed-loop management workflow, transforming raw data into intelligent services.

• Data processing module: This module handles data collection, cleaning, annotation, and feature engineering. It transforms unstructured or structured raw data into high-quality datasets that models can effectively interpret, ensuring the accuracy and validity of input data.

• Model training module: Leveraging computing resources, this module performs iterative training on processed data using selected algorithm frameworks and hyperparameter configurations. Its primary responsibility is to learn inherent patterns within data, resulting in the generation of preliminary model weights and structures.

• Model evaluation and optimization module: This module evaluates model performance against multi-dimensional metrics, such as accuracy, recall, and F1 score. If performance falls short of requirements, the module triggers adjustments to features or hyperparameters. This iterative optimization ensures the model meets deployment standards and maintains strong generalization capabilities.

• Service deployment module: This module encapsulates trained models into standard APIs or embeds them directly into devices. It handles inference requests, delivering high-concurrency, low-latency prediction services. Additionally, it supports critical operational features like model version management and gray releases.

These components are tightly integrated through data pipelines and control flows. The data processing module supplies training sets to the model training module, which then passes the trained artifacts to the model evaluation and optimization module for rigorous validation. Once the model meets performance standards, the service deployment module packages it and exposes it as external services to end users. This clear division of labor standardizes data transfer and enhances the controllability of model iterations. By decoupling data preparation, algorithm iteration, and service O&M, the architecture improves overall system stability and development efficiency. This architecture enables the ML system to provide comprehensive support, guiding users from data insights to intelligent decision-making.

Figure1 Machine learning modules

The input to the ML system is typically preprocessed structured or unstructured data (such as images and text), which is triggered by users through the client by initiating a training job or real-time inference request.

The system first performs data ingestion and authentication to ensure the validity and security of data access. Next, the system enters the feature extraction phase, where it converts the raw data into numerical vectors to serve as the input objects for model computation. Then, the training engine initiates an iterative optimization process based on the configured algorithm. It calculates the predicted values through forward propagation, measures the error using a loss function, and updates the model parameters through backpropagation to gradually reduce the error. In the inference phase, the system loads the trained model weights and performs fast matrix operations on the input data to obtain the prediction. Finally, the system formats and generates the result, and logs the process for future monitoring and optimization.

This process follows the logical sequence of "data preparation - model fitting - result validation" to ensure that the model can effectively learn from the data and generalize to new samples. The key mechanism is to minimize the objective function using mathematical optimization methods, allowing the model to approximate the true distribution. The final prediction provides users with data-driven decision-making basis, implementing automatic and intelligent service processing.

Figure1 Principles of machine learning

Both machine learning and deep learning aim to train models through data to implement intelligent decision-making. They are highly overlapped in application fields and are often mentioned together. However, as a subset of machine learning, deep learning has significant differences in technical implementation.

First, the core difference lies in the feature extraction method. Traditional machine learning relies on manual feature engineering, requiring domain experts to manually extract key features. In contrast, deep learning automatically learns hierarchical features from raw data through multi-layer neural networks, reducing the need for manual intervention. Second, their dependencies on the data volume are different. Traditional algorithms perform well on small- and medium-sized datasets, while deep learning typically requires massive amounts of data to fully leverage its performance advantages. Third, there are differences in compute demands. Deep learning models have a huge number of parameters and rely heavily on high-performance parallel computing resources such as GPUs. In contrast, traditional machine learning has relatively low compute requirements and can run on common CPUs.

These differences ultimately stem from their technical architectures. Deep learning simulates the structure of human brain neurons and captures complex nonlinear relationships through deep networks, while traditional machine learning is mostly based on shallow models and statistical theories.

While deep learning models demonstrate superior accuracy in handling unstructured, complex data like images and speech, they are often limited by low interpretability and high training costs. In contrast, traditional machine learning models offer greater interpretability and efficiency with structured tabular data, making them well-suited for scenarios that require high interpretability, such as financial risk control.

Machine learning is classified based on the learning method and the type of supervision signal. Based on whether labeled data is used in training, machine learning can be classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

• Supervised learning: Its core feature is that the training data contains clear input and output labels. Models make predictions by learning the mapping between inputs and labels. This approach is well-suited for tasks like spam classification, house price prediction, and image recognition. Its key advantages include high prediction accuracy and clearly defined objectives.

• Unsupervised learning: Its core feature is that it relies on unlabeled training data. Models aim to discover intrinsic structures, clustering patterns, or anomalies in the data. This approach is commonly applied to tasks such as customer segmentation, anomaly detection, and data dimensionality reduction. Its main advantages are the elimination of costly manual annotation and the ability to discover hidden patterns.

• Semi-supervised learning: It bridges the gap between supervised and unsupervised learning by leveraging a small set of labeled data alongside a large volume of unlabeled data for model training. This approach is particularly effective in scenarios where acquiring labeled data is costly yet abundant, such as in medical image analysis. Its key advantage lies in maintaining high performance while significantly reducing labeling costs.

• Reinforcement learning: It enables an agent to learn optimal decision-making through trial and error, receiving rewards or penalties based on its actions within an environment. It is well-suited for dynamic decision-making tasks, such as gaming, robotics, and autonomous driving. Its primary advantage is the ability to handle long-term planning and complex sequential decision-making problems.

Figure1 Types of machine learning

Huawei Cloud offers ModelArts, a full-stack machine learning platform that supports the entire AI lifecycle, including data annotation, algorithm development, model training, to model management, deployment, and O&M. ModelArts helps enterprises lower the barriers to AI application development and accelerate intelligent innovation.

• One-stop AI development platform ModelArts

  • Implementation effect: ModelArts provides a visual development interface, significantly accelerates the model development cycle. ModelArts provides diverse Ascend-based compute resources alongside cost-effective training and inference services, ensuring stable and efficient large-scale model training.

  • Related documentation: ModelArts Service Overview

  • Getting Started: You can quickly create a notebook instance on the ModelArts console to experience the entire process of importing data to a model for training.