Mlops Glossary

Browse 45 mlops terms defined in plain English, from the cultural dictionary of computing.

45 Mlops Terms

feature store
A centralized repository for storing, managing, and serving machine learning features — the computed input variables used by ML models. Feature stores ensure...
Mixed Precision Training
Mixed Precision Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for...
MLflow
MLflow is an MLOps platform for experiment tracking, model packaging, and lifecycle management. It is commonly used for recording runs, registering models, and...
Model Architecture
Model Architecture is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Card Detail
Model Card Detail is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Compression
Model Compression is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Distillation
Model Distillation is a training approach where a smaller student model learns from a larger teacher model. It is commonly used for compressing models while...
Model Ensemble
Model Ensemble is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Evaluation
Model Evaluation is an evaluation concept used to measure model quality, robustness, or efficiency. It is commonly used for comparing systems before release or...
Model Explainability
Model Explainability is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Governance
Model Governance is a safety or governance control for limiting harmful, noncompliant, or insecure model behavior. It is commonly used for production AI...
Model Hub
Model Hub is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Inference
Model Inference is the phase where a trained model processes new inputs to produce predictions or generations. It is commonly used for production APIs, batch...
Model Merging
Model Merging is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Parallelism
Model Parallelism is a systems strategy for splitting model computation or parameters across devices. It is commonly used for training and serving workloads...
Model Poisoning
Model Poisoning is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Pruning
Model Pruning is a compression technique that removes parameters, channels, or structures judged less important. It is commonly used for shrinking models for...
Model Quantization
Model Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
Model Registry
Model Registry is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
model serving
The infrastructure and process of deploying trained machine learning models to production so they can receive input data and return predictions in real time or...
Model Serving Detail
Model Serving Detail is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Sharding
Model Sharding is a systems strategy for splitting model computation or parameters across devices. It is commonly used for training and serving workloads that...
Model Stacking
Model Stacking is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Training
Model Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Model Versioning
Model Versioning is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Model Zoo
Model Zoo is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Multi-GPU Training
Multi-GPU Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Non-Autoregressive Model
Non-Autoregressive Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building...
Open Source Model
Open Source Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Open Weight Model
Open Weight Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Post-Training
Post-Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Pre-Training
Pre-Training is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Pretraining Data
Pretraining Data is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Probabilistic Model
Probabilistic Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Quantization Aware Training
Quantization Aware Training is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large...
Scalar Quantization
Scalar Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
State Space Model
State Space Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Teacher Model
Teacher Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...
Training Compute
Training Compute is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Data Detail
Training Data Detail is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Loop
Training Loop is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Training Run
Training Run is a stage of model optimization where weights or behaviors are adjusted from data or feedback. It is commonly used for foundation-model...
Vector Quantization
Vector Quantization is a technique that reduces numerical precision so models use less memory and compute. It is commonly used for deploying large models on...
Weights and Biases
A machine-learning tooling platform commonly used for experiment tracking, model monitoring, and training visualization.
World Model
World Model is a model component or design choice that shapes how information flows through a learned system. It is commonly used for building neural...

Related Topics