DSA Practice — Production ML Engineering Track

Daily Python problem-solving grounded in real ML pipeline scenarios. Every problem connects to a production system — not textbook theory.

Context

Problems are framed around agri-tech computer vision pipelines (fruit grading, defect detection, batch processing, drift monitoring) and FAANG-scale ML systems.

Structure

Each DAY folder contains 5 problems:

• Q1: Warm-up (string/array fundamentals)
• Q2: Data handling (dicts, aggregation, parsing)
• Q3: DS&A pattern (two pointers, sliding window, binary search...)
• Q4: ML systems scenario (batch processing, rate limiting, queues...)
• Q5: Challenge (combines multiple patterns)

Patterns Tracker

Pattern	Status
Counting & Frequency Maps	✅
String Parsing	✅
Nested Dict Aggregation	✅
Filter & Sort	✅
Sliding Window (Fixed)	✅
Sliding Window (Variable)	✅
Two Pointers	✅
Stack	✅
Prefix Sums	✅
Binary Search	✅
Heap / Priority Queue	✅

Target Role

ML Systems Engineer → MLOps Engineer → Staff ML Engineer