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80 results for “machine learning”
A/B Testing (ML)
A/B testing in machine learning is a controlled experiment method that compares two or more model variants in production to determine which delivers superior performance on real-world business metrics.
Active Learning
Active learning is a machine learning paradigm in which the algorithm selectively queries a human annotator for labels on the most informative data points, minimising labelling effort while maximising model performance.
Adversarial Machine Learning
Adversarial machine learning is the study of attacks that exploit weaknesses in machine learning models, such as crafted inputs that cause misclassification, and of the defences designed to make models more robust.
AI Bias
Systematic and unfair discrimination introduced into artificial intelligence systems through biased training data, flawed model design, or problematic deployment decisions, leading to unequal outcomes across demographic groups or categories.
AI Drug Discovery
AI drug discovery applies machine learning, deep learning, and generative modelling to accelerate the identification, design, and optimisation of therapeutic compounds across the pharmaceutical pipeline.
AI in Islamic Finance Malaysia
AI in Islamic finance Malaysia refers to the application of artificial intelligence technologies — including machine learning, natural language processing, and generative AI — to Shariah-compliant financial products, institutions, and regulatory processes in Malaysia.
AI in Malaysian Legal Industry
AI in Malaysia's legal industry encompasses the adoption of machine learning, natural language processing, and generative AI tools by law firms, the judiciary, and legal service providers to automate research, drafting, and compliance tasks.
AI in Malaysian Retail
AI in Malaysian retail encompasses the deployment of machine learning, computer vision, and natural language processing across Malaysia's retail sector, including e-commerce platforms, brick-and-mortar stores, and omnichannel retail operations.
AI Music Generation
AI music generation is the use of machine learning models to compose, arrange, or produce music from text prompts or other inputs, spanning full songs with vocals, instrumental tracks, and sound design.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud platform from AWS that provides an integrated environment for building, training, and deploying machine learning models at scale, incorporating tools for data preparation, model development, MLOps, and generative AI.
Anomaly Detection
A class of machine learning techniques that identifies rare events, observations, or patterns that differ significantly from the majority of data, used for fraud, intrusion, and fault detection.
Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems, encompassing learning, reasoning, problem-solving, perception, and language understanding.
AutoML
AutoML (Automated Machine Learning) is the process of automating the selection, composition, and tuning of machine learning algorithms and pipelines, enabling practitioners to build effective models with reduced manual effort.
Azure AI
Azure AI is Microsoft's integrated portfolio of artificial intelligence services hosted on the Azure cloud platform, encompassing pre-built cognitive APIs, a managed machine learning platform, large language model access, and enterprise AI development tools.
Bayesian Inference
Bayesian inference is a statistical method that uses Bayes' theorem to update the probability of a hypothesis as new evidence becomes available, providing a principled framework for reasoning under uncertainty.
Causal AI
Causal AI is an approach to artificial intelligence that incorporates causal reasoning into machine learning models, enabling them to go beyond correlation-based prediction to answer questions about interventions and counterfactual outcomes.
Comet ML
Comet ML is a cloud-based MLOps platform for tracking machine learning experiments, managing model versions, monitoring production models, and evaluating large language model applications.
Continual Learning
Continual learning is a machine learning paradigm in which models incrementally acquire knowledge from sequential tasks or data streams without forgetting previously learned information, addressing the stability-plasticity trade-off inherent in neural networks.
Contrastive Learning
Contrastive learning is a self-supervised machine learning paradigm that trains models to produce similar representations for related data pairs and dissimilar representations for unrelated pairs, enabling powerful feature learning without labelled data.
Core ML
Core ML is Apple's on-device machine learning framework that enables iOS, macOS, watchOS, and tvOS applications to integrate pre-trained models for tasks including image classification, natural language processing, and sound analysis.
Cosine Similarity
Cosine similarity is a measure of similarity between two non-zero vectors equal to the cosine of the angle between them, widely used to compare embeddings in search and machine learning.
Data Labelling
Data labelling is the process of attaching meaningful tags, classes, or annotations to raw data so that supervised machine learning models can learn to predict those labels on unseen examples.
Data Pipeline
A data pipeline is an automated sequence of processes that ingests, transforms, and delivers data from source systems to destination systems for analysis, machine learning, or operational use.
DataOps
DataOps is an engineering methodology that applies agile, DevOps, and lean manufacturing principles to data pipelines, aiming for rapid, reliable, and repeatable delivery of analytics and machine learning data.
DataRobot
An American enterprise AI platform company that provides automated machine learning, model deployment, monitoring, and generative AI tools, headquartered in Boston, Massachusetts.
Deep Learning
Deep learning is a subfield of machine learning that uses multi-layered artificial neural networks to learn hierarchical representations from data, enabling state-of-the-art performance across vision, language, and speech tasks.
Differential Privacy
Differential privacy is a mathematical framework for analysing data that guarantees the output of a computation reveals little about any single individual, achieved by adding calibrated random noise to limit each record's influence.
Domain Adaptation
Domain adaptation is a machine learning technique that transfers a model trained on a labelled source domain to perform effectively on a related but distinct target domain with limited or no labelled target data, addressing distribution shift between domains.
Embedding
An embedding is a dense numerical vector representation of data — such as text, images, or audio — that encodes semantic meaning in a continuous high-dimensional space, enabling machine learning models to measure similarity and relationships.
Explainable AI
Explainable AI (XAI) refers to methods and techniques that make the decisions and predictions of artificial intelligence systems interpretable and understandable to human users, addressing the opacity of complex machine learning models.
Feature Store
A centralised data platform for storing, serving, and managing machine learning features so that they can be reused consistently across training and online inference.
Federated Learning
Federated learning is a machine learning paradigm in which a model is trained across multiple decentralised devices or servers holding local data, without exchanging the raw data itself, preserving privacy while enabling collaborative model improvement.
Few-Shot Learning
Few-shot learning is a machine learning paradigm in which a model learns to perform new tasks or recognise new classes from only a small number of labelled training examples, often just one to five samples per class.
Fine-Tuning
The process of further training a pre-trained machine learning model on a smaller, task-specific dataset to adapt its weights for a particular domain, task, or desired behaviour.
Fraud Detection
Fraud detection is the application of data analysis, machine learning, and AI to identify deceptive or unauthorised transactions, activities, and behaviours in financial, digital, and commercial systems.
Generative Adversarial Network
A generative adversarial network (GAN) is a class of machine learning framework in which two neural networks, a generator and a discriminator, compete against each other to produce synthetic data indistinguishable from real examples.
Google Vertex AI
Google Vertex AI is a unified machine learning platform on Google Cloud that consolidates data preparation, model training, deployment, and monitoring for both custom-built models and Google's foundation models including Gemini.
Gradient Boosting
A machine learning ensemble technique that builds predictive models sequentially, where each new model corrects the errors of its predecessors using gradient descent optimisation.
Gradient Descent
Gradient descent is an iterative optimisation algorithm that minimises a loss function by repeatedly updating model parameters in the direction of the steepest descent, as defined by the negative gradient.
Hugging Face
An American AI company and open-source platform that hosts machine learning models, datasets, and applications, widely described as the "GitHub of machine learning" for its role as the central repository of the open AI community.
Hyperparameter Tuning
The process of selecting optimal configuration values for a machine learning model's external parameters using methods such as grid search, random search, and Bayesian optimisation.
Inference (Machine Learning)
Inference is the phase in which a trained machine learning model is used to generate predictions or outputs from new input data, distinct from the earlier training phase.
JAX
JAX is an open-source numerical computing library from Google that combines NumPy-style array programming with automatic differentiation and just-in-time compilation, used to train large-scale machine learning models on GPUs and TPUs.
K-Means Clustering
K-means clustering is an unsupervised machine learning algorithm that partitions a dataset into k groups by minimising the sum of squared distances between data points and their assigned cluster centroids.
Labelbox
Labelbox is an American AI data labeling and model evaluation platform that enables organisations to annotate training datasets, manage labeling workflows, and curate high-quality data for machine learning development.
Machine Learning
Machine learning is a subfield of artificial intelligence in which systems improve their performance on tasks through experience — by automatically learning patterns from data rather than following explicitly programmed rules.
Meta-Learning
A machine learning paradigm in which models learn how to learn, acquiring inductive biases across a distribution of tasks so they can adapt rapidly to new tasks with minimal data.
Mixture of Experts
Mixture of Experts (MoE) is a machine learning architecture in which a model routes each input to a small subset of specialised sub-networks called experts, enabling large model capacity at a fraction of the compute cost.
MLflow
An open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, model packaging, a model registry, and deployment.
MLOps
A set of practices and tools that combine machine learning, DevOps, and data engineering to automate and operationalise the full lifecycle of ML models from development through production deployment and monitoring.
Model Cards
Model cards are structured documentation sheets accompanying machine learning models that disclose intended uses, performance characteristics, training data, limitations, and ethical considerations.
Model Compression
Model compression is a set of techniques that reduce the size, memory footprint, and computational cost of machine learning models while preserving predictive accuracy, enabling deployment on resource-constrained hardware.
Model Registry
A model registry is a centralised system that catalogues, versions, and governs trained machine learning models throughout their lifecycle, supporting reproducibility, deployment, and compliance.
Model Serving
Model serving is the discipline of deploying trained machine learning models behind APIs or runtimes so that production applications can request predictions at scale with predictable latency, throughput, and reliability.
Monte Carlo Methods
A broad class of computational algorithms that use repeated random sampling to obtain numerical results, widely used in machine learning for Bayesian inference, reinforcement learning, and uncertainty estimation.
Multi-Task Learning
Multi-task learning is a machine learning approach in which a model is trained simultaneously on multiple related tasks, using shared representations to improve generalisation and data efficiency compared to training separate single-task models.
Neptune.ai
Neptune.ai was an MLOps experiment tracking and metadata management platform that provided data science teams with tools to log, compare, and reproduce machine learning experiments at scale; the company was acquired by OpenAI in 2025.
Neural Network
A neural network is a computational model inspired by biological brains, composed of interconnected layers of nodes that learn patterns from data through weighted connections.
ONNX (Open Neural Network Exchange)
An open standard format for representing machine learning models that enables interoperability between deep learning frameworks, runtimes, and hardware platforms.
Overfitting
Overfitting is a modelling error in machine learning where a model learns the training data too closely, including its noise, and consequently performs poorly on new, unseen data.
Predictive Maintenance
Predictive maintenance is the use of sensor data, statistical modelling, and machine learning to forecast equipment failures before they occur, enabling repairs to be scheduled precisely when needed.
PyTorch
PyTorch is an open-source machine learning framework, originally developed by Meta AI, that provides tensor computation with GPU acceleration and a dynamic computational graph for building and training deep neural networks.
Random Forest
Random forest is an ensemble machine learning algorithm that builds many decision trees on bootstrapped samples and aggregates their predictions to improve accuracy and reduce overfitting.
Regularisation (Machine Learning)
Regularisation is a collection of techniques in machine learning that constrain models during training to reduce overfitting and improve generalisation to unseen data.
Reinforcement Learning
A machine learning paradigm in which an agent learns to make sequential decisions by interacting with an environment and optimising for cumulative reward through trial and error.
Reinforcement Learning from Human Feedback
A machine learning technique that trains a reward model from human preference data and uses it to align large language models with human values, safety requirements, and intended behaviour through reinforcement learning.
Scikit-learn
Scikit-learn is an open-source Python library for classical machine learning, providing accessible and consistent implementations of classification, regression, clustering, and data-preprocessing algorithms built on NumPy and SciPy.
Self-Supervised Learning
A machine learning training paradigm in which a model generates its own supervisory signal from unlabelled data by solving pretext tasks, learning rich representations without human-annotated labels.
Shadow Mode
Shadow mode is a machine learning deployment strategy in which a new model processes live production traffic in parallel with the existing model, capturing outputs for evaluation without affecting users or business operations.
Support Vector Machine
A support vector machine (SVM) is a supervised machine learning algorithm that finds the optimal hyperplane separating data points of different classes by maximising the margin between the boundary and the nearest training examples.
Synthetic Data
Synthetic data is artificially generated data that mimics the statistical properties of real datasets, created using generative AI or simulations to train machine learning models without exposing sensitive personal information.
Tensor Processing Unit
A tensor processing unit (TPU) is a custom application-specific integrated circuit developed by Google for accelerating machine learning workloads, particularly neural network training and inference.
TensorFlow
TensorFlow is an open-source machine learning platform developed by Google that supports the full lifecycle of building, training, and deploying models across servers, mobile devices, browsers, and edge hardware.
TensorFlow Lite
TensorFlow Lite is an open-source deep learning framework from Google for running optimised machine learning models on mobile phones, microcontrollers, and other edge devices.
Time Series Forecasting
Time series forecasting is the application of statistical and machine learning methods to predict future values of a sequence of observations indexed in time, such as sales, demand, electricity load, or financial prices.
TinyML
TinyML is a field of machine learning focused on running machine learning models on microcontrollers and other resource-constrained edge devices that typically operate with milliwatts of power and kilobytes of memory.
Transfer Learning
Transfer learning is a machine learning technique in which a model pre-trained on one task or dataset is adapted for a different but related task, enabling high performance with significantly less data and compute than training from scratch.
Weights and Biases
Weights and Biases (W&B) is a machine learning developer platform for experiment tracking, model versioning, dataset management, and collaborative model evaluation used by over 200,000 ML practitioners worldwide.
XGBoost
XGBoost (Extreme Gradient Boosting) is an open-source machine learning library that provides a fast, regularised gradient boosting framework, widely used for classification, regression, and ranking on tabular data.
Zero-Shot Learning
Zero-shot learning is a machine learning paradigm in which a model makes accurate predictions on categories it has never seen during training by leveraging semantic descriptions or attribute representations.