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Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
Download IgniteARCHITECTURAL BACKBONE
CORE FEATURES
DATA PLACEMENT & PROCESSING
OPERATIONAL EXCELLENCE
ADDITIONAL USE CASES
AI/ML Feature Stores
With Apache Ignite
Real-time OR Accurate? Choose Both.
Zero training/serving skew with low-latency feature retrieval
Zero training/serving skew with low-latency feature retrieval
The Trade-off Problem
Traditional ML feature stores force an impossible choice: batch-computed features for training (accurate but stale), or cached features for serving (fast but risk training/serving skew). Models trained on batch features fail when serving features diverge.
Training/serving skew degrades model accuracy in production. Batch feature pipelines introduce latency that makes real-time predictions impossible. Cached features create eventual consistency risks where models see different feature values during training versus serving.
How Apache Ignite Solves This
Apache Ignite provides point-in-time consistency across training and serving through MVCC snapshots
Zero Training/Serving Skew
MVCC snapshot isolation enables training jobs to read feature values at specific points in time while serving endpoints read current features with strong consistency. Guarantees training and serving see the same feature semantics. No eventual consistency windows that degrade model accuracy.
Low-Latency Inference
Memory-first architecture delivers low-latency feature retrieval for real-time inference. RecordView API provides direct partition-aware access without batch preprocessing. Table schema management supports adding features without breaking models. SQL access enables feature exploration during development.
Architecture Pattern
Feature Store With Point-In-Time Consistency
Training pipelines read feature snapshots at specific timestamps. Serving endpoints read current features with strong consistency. Eliminates training/serving skew through MVCC.
Integration Pattern: Feature engineering pipelines write computed features to Apache Ignite tables. Training jobs specify snapshot timestamps for historical consistency. Serving endpoints read current features through RecordView API for real-time inference.
Consistency Model: Snapshot isolation ensures training reads consistent feature values at point-in-time. Consensus replication ensures serving reads strongly consistent current values. No eventual consistency windows between feature writes and reads.
Performance Characteristics: Memory-first storage delivers low-latency feature retrieval for online inference. Partition-aware routing minimizes feature lookup overhead. Batch training jobs read historical snapshots without impacting serving latency.
When This Pattern Works
This architecture pattern is best for:
- Real-time ML inference requiring low-latency feature access
- Systems where training/serving skew degrades model accuracy
- Feature stores needing point-in-time consistency for training
- Applications requiring feature versioning and schema evolution
Example Use Cases:
- Recommendation Engines: User and item features with consistent snapshots for model training and low-latency serving
- Fraud Detection: Transaction features computed in real-time with historical consistency for model retraining
- Personalization: User profile features updated continuously while maintaining point-in-time consistency for A/B tests
Key Benefits
Zero Training/Serving Skew
MVCC snapshots guarantee training sees consistent feature values at point-in-time. Serving reads current features with strong consistency. Models trained on historical snapshots match serving semantics. No eventual consistency risks that degrade model accuracy.
Low-Latency Inference
Memory-first storage delivers low-latency feature retrieval for online inference. Partition-aware routing minimizes lookup overhead. RecordView API provides direct access without query parsing. Enables real-time ML predictions without batch preprocessing.
Feature Evolution
Table schema management supports adding features without breaking models. SQL access enables feature exploration during development. Version control for feature definitions. Schema evolution doesn't require data migration.
Operational Simplicity
Single platform replaces separate feature computation, storage, and serving layers. Eliminates batch export pipelines for training data. No cache warming or TTL management. Reduces operational complexity of ML infrastructure.
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Use Cases Overview