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Model Training
What is model training in MLOps?
Model training in
MLOps
happens as part of a model training pipeline.
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AutoML
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Backfill features
Backfill training data
Backpressure for feature stores
Batch Inference Pipeline
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CI/CD for MLOps
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DAG Processing Model
Data Compatibility
Data Contract
Data Lakehouse
Data Leakage
Data Modeling
Data Partitioning
Data Pipelines
Data Quality
Data Transformation
Data Type (for features)
Data Validation (for features)
Data-Centric ML
Dimensional Modeling and Feature Stores
Downstream
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ELT
ETL
Embedding
Encoding (for Features)
Entity
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Feature
Feature Engineering
Feature Freshness
Feature Function
Feature Groups
Feature Logic
Feature Monitoring
Feature Pipeline
Feature Platform
Feature Reuse
Feature Selection
Feature Service
Feature Store
Feature Type
Feature Value
Feature Vector
Feature View
Filtering
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Generative AI
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Hyperparameter
Hyperparameter Tuning
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Idempotent ML Pipelines
Inference Data
Inference Logs
Inference Pipeline
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LLMs - Large Language Models
Lagged features
Latent Space
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ML
ML Artifacts (ML Assets)
ML Pipeline
MLOps
MVPS
Model Architecture
Model Bias
Model Deployment
Model Development
Model Evaluation (Model Validation)
Model Governance
Model Interpretability
Model Monitoring
Model Performance
Model Quantization
Model Registry
Model-Centric ML
Model-Dependent Transformations
Model-Independent Transformations
Monolithic ML Pipeline
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Natural Language Processing (NLP)
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Offline Store
On-Demand Features
On-Demand Transformation
Online Inference Pipeline
Online Store
Online-Offline Feature Skew
Online-Offline Feature Store Consistency
Orchestration
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Pandas UDF
Point-in-Time Correct Joins
Precomputed Features
Python UDF
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Real-Time Machine Learning
Representation Learning
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SQL UDF in Python
Schema
Similarity Search
Skew
Splitting Training Data
Streaming Feature Pipeline
Streaming Inference Pipeline
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Test Set
Time travel (for features)
Train (Training) Set
Training Data
Training Pipeline
Training-Inference Skew
Transformation
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Upstream
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Validation Set
Vector Database
Versioning (of ML Artifacts)
