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29 articles in this section
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.
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.
CUDA
NVIDIA's parallel computing platform and programming model that lets developers use GPUs for general-purpose computation, underpinning most modern deep learning frameworks.
Data Augmentation
A set of techniques that expand a training dataset by creating modified copies of existing examples, helping deep learning models generalise better and reducing overfitting.
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.
Edge AI
Edge AI is the deployment of artificial intelligence algorithms and inference workloads directly on local devices or edge computing nodes rather than in centralised cloud data centres, enabling low-latency, privacy-preserving, and bandwidth-efficient AI applications.
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.
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.
Knowledge Distillation
Knowledge distillation is a model compression technique in which a smaller student neural network is trained to replicate the behaviour of a larger, more capable teacher model, enabling deployment of efficient models that approximate teacher-level performance.
LangChain
LangChain is an open-source framework for building applications powered by large language models, providing composable abstractions for chaining LLM calls with tools, memory, and data retrieval in Python and JavaScript.
Langfuse
Langfuse is an open-source LLM engineering platform that provides observability, tracing, prompt management, evaluation, and dataset tooling for teams building applications on top of large language models.
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 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 Pruning
A model compression technique that removes redundant or low-importance parameters from a neural network to reduce size, memory footprint, and inference latency while preserving accuracy.
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.
Neural Architecture Search
Neural architecture search is the automated design of neural network architectures using search algorithms, reinforcement learning, or gradient-based methods to discover models that meet target accuracy, latency, and size constraints.
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.
OpenVINO
OpenVINO is an open-source toolkit developed by Intel for optimising and deploying deep learning inference across Intel hardware, including CPUs, GPUs, Neural Processing Units, and FPGAs, with broad support for major AI frameworks and model formats.
Parameter-Efficient Fine-Tuning
A family of techniques that adapts a pretrained language or vision model to a downstream task by training only a small fraction of its parameters, dramatically reducing compute, memory, and storage requirements compared to full fine-tuning.
Quantisation
Quantisation is a model compression technique that reduces the numerical precision of a neural network's weights and activations from high-bit floating-point formats to lower-bit representations, decreasing memory usage and accelerating inference with minimal accuracy loss.
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 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.
Vector Database
A specialised database system that stores data as high-dimensional numerical vectors and enables fast approximate nearest-neighbour search, forming the retrieval backbone of semantic search and RAG systems.