Data & Storage
The forgotten half of AI infrastructure — training data pipelines, GPU feeding, storage systems, and vector databases.
Training Data Pipelines
Data Collectioncoming soon
Common Crawl processing, domain-specific scraping, licensing and compliance.
Cleaning & Filteringcoming soon
Language detection, quality scoring, deduplication (MinHash, exact, semantic), PII removal, toxicity filtering.
Data Mixingcoming soon
Balancing domains (code, math, web, books), curriculum strategies, upsampling/downsampling.
Tokenization at Scalecoming soon
BPE training, SentencePiece/tiktoken, parallelized tokenization pipelines.
Synthetic Data Generationcoming soon
Model-generated training data — scheduling, dedup against real data, quality verification.
Data Loading & GPU Feeding
PyTorch DataLoadercoming soon
num_workers, prefetching, pin_memory — and their limits at scale.
Streaming Datasetscoming soon
Mosaic StreamingDataset, WebDataset, HuggingFace streaming — reading from cloud without full download.
GPU-Accelerated Preprocessingcoming soon
NVIDIA DALI for vision and multimodal workloads.
Sequence Packingcoming soon
Combining variable-length sequences into fixed-length examples to eliminate padding waste.
Data Loading at Scalecoming soon
Coordinating thousands of workers, shuffle buffers, avoiding I/O hotspots.
Storage Systems
Parallel File Systemscoming soon
Lustre, GPFS/Spectrum Scale — metadata performance, striping, tuning for AI workloads.
Cloud Object Storagecoming soon
S3, GCS, Azure Blob — high throughput / high latency, and caching layers (Alluxio, JuiceFS).
Checkpoint Storage Designcoming soon
Burst write patterns, NVMe staging, async upload, tiered retention policies.
Model Artifact Distributioncoming soon
Distributing multi-GB model files across a cluster — cache hierarchies, P2P distribution.
Vector Databases
Index Structurescoming soon
Flat (exact), IVF (partition-based), HNSW (graph-based), DiskANN (SSD-resident, billion-scale).
System Architecturecoming soon
Milvus, Faiss, Qdrant — sharding, replication, consistency models.
Performance Tuningcoming soon
Latency vs recall tradeoff, batch query optimization, metadata predicate filtering.
Operational Concernscoming soon
Index build time, incremental updates, compaction, backup and restore.
Dataset Management
Dataset Versioningcoming soon
DVC, LakeFS, Delta Lake, Iceberg — Git-like branching for data lakes.
Data Lineagecoming soon
Tracking which sources contributed to which training run — reproducibility and debugging.
Data Quality Monitoringcoming soon
Statistical drift detection, label auditing, coverage analysis.
Data Flywheelcoming soon
Using production outputs to improve training data — feedback loops, annotation platforms.