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Data Pipelines

What are data pipelines?

Data pipelines are orchestrated programs that move data from one system to another while also performing transformations on the data either before it has been copied to the target system (ELT) or after it has been copied (ETL). Data pipelines are a key building block in data engineering, as they enable data to flow from operational databases, where the data is generated, to analytical data warehouses, lakehouses, and data lakes where the data is analyzed and used for machine learning. The primary goal of a data pipeline is to automate and streamline the data flow, making it more efficient and reliable.

Without data pipelines (i.e., batch or streaming feature pipelines) machine learning systems can only work on static data and a model cannot be automated to generate value through automating predictions on new (inference) data. Compared to batch data pipelines, streaming data pipelines can produce fresher data (features), which is important for real-time ML.

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AI Lakehouse
AI Pipeline
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
Compound AI Systems
Context Window for LLMs
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DAG Processing Model
Data Compatibility
Data Contract
Data Lakehouse
Data Leakage
Data Modeling
Data Partitioning
Data Quality
Data Transformation
Data Type (for features)
Data Validation (for features)
Data Warehouse
Data-Centric ML
Dimensional Modeling and Feature Stores
Downstream
Query Engines
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ELT
ETL
Embedding
Encoding (for Features)
Entity
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Feature
Feature Data
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
Gradient Accumulation
Grouped Query Attention
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Hallucinations in LLMs
Hyperparameter
Hyperparameter Tuning
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Idempotent Machine Learning Pipelines
In Context Learning (ICL)
Inference Data
Inference Logs
Inference Pipeline
Instruction Datasets for Fine-Tuning LLMs
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LLM Code Interpreter
LLM Temperature
LLMOps
LLMs - Large Language Models
Lagged features
LangChain
Latent Space
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ML
ML Artifacts (ML Assets)
MLOps
MLOps Platform
MVPS
Machine Learning Infrastructure
Machine Learning Logs
Machine Learning Observability
Machine Learning Pipeline
Machine Learning Systems
Model Architecture
Model Bias
Model Deployment
Model Development
Model Evaluation (Model Validation)
Model Governance
Model Inference
Model Interpretability
Model Monitoring
Model Performance
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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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KServe
PagedAttention
Pandas UDF
Parameter-Efficient Fine-Tuning (PEFT) of LLMs
Point-in-Time Correct Joins
Precomputed Features
Prompt Engineering
Prompt Store
Prompt Tuning
Python UDF
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RLHF - Reinforcement Learning from Human Feedback
Real-Time Machine Learning
Recommender System
Representation Learning
Retrieval Augmented Generation (RAG) for LLMs
RoPE Scaling
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SQL UDF in Python
Sample Packing
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)
vLLM