nlp-systems-review

Applied NLP & Data Engineering Systems

A practical, code-first study guide and architectural reference for building scalable data pipelines and Information Retrieval (IR) systems.

We hope to bridge the gap between theoretical algorithms and production-grade system design. Rather than relying on black-box libraries, these modules implement core algorithmic examples from scratch to demonstrate their mathematical TRADE-OFFS, EDGE CASES, and SCALING LIMITATIONS under extreme data constraints.

Design Philosophy

• Pedagogical Output: Every script is instrumented with extensive logging to demonstrate the “Why” and “How” of algorithm behavior, forcing edge cases and failures to highlight system boundaries.
• Standard Library First: Logic is implemented in raw Python/NumPy whenever possible to expose the underlying mechanics (e.g., building an Inverted Index or LSH Bucket system from scratch).
• Resource Awareness: Every module considers memory constraints ($O(1)$ space) and computational complexity ($O(N)$ vs $O(N^2)$) as high priorities.

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Repository Structure

├── text_comparison/    # Identity, Fuzzy, Syntactic, Lexical, and Semantic matching
├── data_sampling/      # Stratification, Weak Supervision, and Active Learning
├── data_analytics/     # ETL, Window Functions, and Strategic Visualization
├── mini_lessons/       # Specialized deep-dives (Regex ReDoS, Clustering)
├── metrics/            # (WIP): Evaluation frameworks (Precision/Recall, Kappa, etc.)
├── sharding/           # (WIP): Map-Reduce shards for Big Data
└── python_practice/    # (WIP): Performance engineering (GIL, Generators, more)

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Module 1: Big Data Architecture (/sharding)

The Question: “How do you process data larger than RAM?”

• Consistent Hashing: Implementing deterministic partitioning to group data without $O(N^2)$ overhead.
• Heuristic Filtering: Demonstrating the “Map Phase” trick of string-matching before JSON parsing to save hours of CPU time.
• Use Case: Forensic analysis of 80TB security logs to identify credential-stuffing IPs on a single 64GB machine.

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Module 2: The Text Comparison Spectrum (/text_comparison)

The Question: “How do you define and measure similarity?”

Level 1: Exact Identity & Bloom Filters

• Use Case: Checking if a URL has been crawled before or verifying file integrity (checksums).

  Level 2: Fuzzy Matching & Metric Search

• Use Case: Search bar autocorrect (“iphne” ➔ “iphone”) and resolving duplicate “John Smith” records in a CRM.

  Level 3: Syntactic Similarity & LSH

• Use Case: Detecting plagiarism in long-form articles or deduplicating massive web-scraped datasets (CommonCrawl).

  Level 4: Lexical Search (IR) & BM25

• Use Case: E-commerce product search and technical log retrieval where exact part numbers or error codes matter.

  Level 5: Semantic Search & Hybrid RRF

• Use Case: Building a Retrieval-Augmented Generation (RAG) system that understands intent (“computer broke” ➔ “laptop repair guide”).

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Module 3: Corpus Curation & Data Sampling (/data_sampling)

The Question: “How do you select high-value data for human annotation?”

Level 1: Stratified Baselines

• Use Case: Creating a statistically honest “Golden Test Set” that doesn’t ignore rare 1% classes like “Fraud.”

  Level 2: Weak Supervision (Heuristics)

• Use Case: “Cold-starting” a new feature (e.g., “Cancel Subscription”) with 10 million raw logs and zero labels.

  Level 3: Uncertainty Sampling

• Use Case: Identifying the “Decision Boundary” to efficiently teach a model the difference between “Downgrade” and “Cancel.”

  Level 4: Geometric Diversity (Core-Set)

• Use Case: Discovering “Unknown Unknowns” by sampling from every semantic neighborhood in a dataset.

  Level 5: Hybrid Active Learning

• Use Case: Production-grade sampling that picks “Confusing” data while automatically rejecting unlearnable “Noise.”

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Module 4: Data Analytics & Strategic Insights (/data_analytics)

The Question: “Can you find the story in the data?”

• Step 1: Synthetic Sabotage: Programmatically injecting “Real-World Mess” (Nested JSON, Unicode noise, date chaos).
• Step 2: The Data Janitor: Professional ETL patterns using pd.json_normalize and schema coalescing.
• Step 3: Window Functions: Using .transform() for category-relative Z-scores to find “Hidden Gems.”
• Step 4: Applied Science: Time-series resampling and “Rating Decay” detection.
• Step 5: Visual Storytelling: Using Faceting and KDE distributions to justify business decisions.

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More modules will come…