Machine Learning in Production

3.

Data Engineering for Production

3.1.

Data Architecture Design

3.1.1.

Data Flow Design

3.1.2.

System Integration Points

3.1.3.

Data Governance Framework

3.2.

Data Ingestion and Collection

3.2.1.

Batch Data Sources

3.2.1.1.

Data Warehouses

3.2.1.2.

File-based Data

3.2.1.2.1.

3.2.1.2.2.

3.2.1.2.3.

3.2.1.3.

Database Systems

3.2.2.

Streaming Data Sources

3.2.2.1.

3.2.2.1.1.

3.2.2.1.2.

3.2.2.1.3.

3.2.2.2.

Real-time Data APIs

3.2.2.3.

IoT Data Streams

3.2.3.

Data Collection Strategies

3.2.3.1.

Pull vs Push Mechanisms

3.2.3.2.

Data Sampling Techniques

3.2.3.3.

Data Collection Frequency

3.3.

Data Validation and Quality Assurance

3.3.1.

Schema Validation

3.3.1.1.

Data Type Checks

3.3.1.2.

Field Presence and Constraints

3.3.1.3.

Schema Evolution Management

3.3.2.

Statistical Property Checks

3.3.2.1.

Distribution Analysis

3.3.2.2.

Outlier Detection

3.3.2.3.

Data Range Validation

3.3.3.

Data Quality Metrics

3.3.3.1.

3.3.3.2.

3.3.3.3.

3.3.3.4.

3.3.4.

Anomaly Detection

3.3.4.1.

Automated Anomaly Detection Tools

3.3.4.2.

Manual Review Processes

3.3.4.3.

Anomaly Response Procedures

3.4.

Data Storage Solutions

3.4.1.

3.4.1.1.

Raw Data Storage

3.4.1.2.

Unstructured and Semi-structured Data

3.4.1.3.

Data Lake Architecture

3.4.2.

Data Warehouses

3.4.2.1.

Structured Data Storage

3.4.2.2.

Query Optimization

3.4.2.3.

3.4.3.

3.4.3.1.

Cloud Storage Solutions

3.4.3.2.

Data Partitioning Strategies

3.4.4.

Database Selection

3.4.4.1.

Relational Databases

3.4.4.2.

NoSQL Databases

3.4.4.3.

Time Series Databases

3.5.

Production-Ready Feature Engineering

3.5.1.

Feature Engineering Pipelines

3.5.1.1.

Modular Pipeline Design

3.5.1.2.

Reusable Feature Components

3.5.1.3.

Pipeline Testing

3.5.2.

Handling Missing Data

3.5.2.1.

Imputation Techniques

3.5.2.2.

Data Exclusion Strategies

3.5.2.3.

Missing Data Patterns

3.5.3.

Data Transformation and Scaling

3.5.3.1.

Normalization and Standardization

3.5.3.2.

Encoding Categorical Variables

3.5.3.3.

Feature Selection and Extraction

3.5.3.4.

Dimensionality Reduction

3.5.4.

Feature Validation

3.5.4.1.

Feature Quality Checks

3.5.4.2.

Feature Drift Detection

3.5.4.3.

Feature Importance Analysis

3.6.

3.6.1.

Role of a Feature Store

3.6.1.1.

Centralized Feature Management

3.6.1.2.

Feature Reuse Across Models

3.6.1.3.

Feature Discovery

3.6.2.

Online vs Offline Feature Serving

3.6.2.1.

Real-time Feature Serving

3.6.2.2.

Batch Feature Serving

3.6.2.3.

Hybrid Serving Patterns

3.6.3.

Training-Serving Skew Prevention

3.6.3.1.

Consistent Feature Computation

3.6.3.2.

Validation of Feature Consistency

3.6.3.3.

Point-in-time Correctness

3.6.4.

Feature Store Implementation

3.6.4.1.

Open Source Solutions

3.6.4.2.

Managed Feature Store Services

3.6.4.3.

Custom Implementation Considerations

3.7.

Data Versioning and Lineage

3.7.1.

Data Versioning Strategies

3.7.1.1.

Snapshot-based Versioning

3.7.1.2.

Delta-based Versioning

3.7.1.3.

Semantic Versioning for Data

3.7.2.

Tools for Data Versioning

3.7.2.1.

Data Version Control

3.7.2.2.

3.7.2.3.

Git-based Solutions

3.7.3.

Tracking Data Provenance

3.7.3.1.

Metadata Management

3.7.3.2.

Data Lineage Visualization

3.7.3.3.

Impact Analysis

3.7.4.

Data Catalog Management

3.7.4.1.

3.7.4.2.

Data Documentation

3.7.4.3.

Data Usage Tracking

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4. Model Development for Production