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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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Auto-regressive Models
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
Context Window for LLMs
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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
Fine-Tuning LLMs
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Generative AI
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Hallucinations in LLMs
Hyperparameter
Hyperparameter Tuning
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Idempotent ML Pipelines
In Context Learning (ICL)
Inference Data
Inference Logs
Inference Pipeline
Instruction Datasets for Fine-Tuning LLMs
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LLM Code Interpreter
LLMOps
LLMs - Large Language Models
Lagged features
LangChain
Latent Space
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ML
ML Artifacts (ML Assets)
ML Pipeline
ML Systems
MLOps
MVPS
Model Architecture
Model Bias
Model Deployment
Model Development
Model Evaluation (Model Validation)
Model Governance
Model Inference
Model Interpretability
Model Monitoring
Model Performance
Model Quantization
Model Registry
Model-Centric ML
Model-Dependent Transformations
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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
Parameter-Efficient Fine-Tuning (PEFT) of LLMs
Point-in-Time Correct Joins
Precomputed Features
Prompt Engineering
Prompt Tuning
Python UDF
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RLHF - Reinforcement Learning from Human Feedback
Real-Time Machine Learning
Representation Learning
Retrieval Augmented Generation (RAG) for LLMs
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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
Theory-of-Mind Tasks
Time travel (for features)
Train (Training) Set
Training Data
Training Pipeline
Training-Inference Skew
Transformation
Two-Tower Embedding Model
Types of Machine Learning
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Upstream
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Validation Set
Vector Database
Versioning (of ML Artifacts)
