Data flow in AI apps

Nee Swiggy la food order pannum bodhu, app "You might also like..." nu recommend pannum. Indha recommendation eppadi varudhu? 🍕

Behind the scenes la data oru amazing journey pogudhu — nee click panradhu la irundhu, AI model prediction panradhu varai. Idhu dhaan Data Flow in AI Applications.

Indha article la oru AI app la data eppadi flow aagudhu — step by step paapom. Real-world example use panni, clear ah purinjidam! 💡

AI app la data flow ah 8 stages ah break pannalam:

Stage 1: Data Collection 📥

Users clicks, sensors, APIs, databases — raw data collect aagudhu.

Stage 2: Data Ingestion 🔄

Collected data ah system ku bring pannudhu — streaming (real-time) or batch (scheduled).

Stage 3: Data Processing 🧹

Raw data clean pannudhu — remove duplicates, fix errors, standardize formats.

Stage 4: Data Storage 💾

Processed data store pannudhu — data lake, warehouse, or feature store.

Stage 5: Feature Engineering 🔧

Raw data la irundhu AI-friendly features create pannudhu.

Stage 6: Model Training 🧠

Features use panni ML model train pannudhu.

Stage 7: Model Serving 🚀

Trained model predictions serve pannudhu — API through users ku.

Stage 8: Monitoring & Feedback 📊

Model performance track pannudhu, new data collect pannudhu — cycle continues!

Data flow ah oru Biryani restaurant maari think pannunga! 🍚

🧅 Collection = Raw ingredients vaanguradhu (rice, chicken, spices from different shops)

🚛 Ingestion = Kitchen ku transport panradhu

🔪 Processing = Wash, cut, marinate panradhu (cleaning)

🏪 Storage = Fridge la organized ah store panradhu

👨‍🍳 Feature Engineering = Recipe ku theva aana proportion la measure panradhu

🔥 Training = Cooking process — trial and error, taste adjust

🍽️ Serving = Customer table la hot biryani serve panradhu

⭐ Monitoring = Customer feedback — "too spicy", "perfect!" — next batch improve

Every stage miss pannaa, biryani taste aagadhu! Same way, every data flow stage critical for AI quality! 🎯

Swiggy la food recommendation eppadi work aagudhu — full flow paapom:

Step 1: You open the app 📱

• Your location, time, past orders — collected

Step 2: Data ingested

• Real-time: Your current session data streams in
• Batch: Your historical order data already processed

Step 3: Processing

• Remove duplicate events, standardize restaurant IDs
• Merge your profile with restaurant data

Step 4: Feature Engineering 🔧

• "User prefers biryani" (ordered 15 times last month)
• "User orders lunch 12-1 PM"
• "User's avg order value: ₹350"
• "Nearby restaurants with >4.2 rating"

Step 5: Model Prediction 🧠

• Recommendation model takes features → ranks restaurants
• "90% chance user will like Meghana Biryani"

Step 6: Served to you 🍽️

• Top recommendations appear on your home screen

Step 7: You click/order → feedback 🔄

• Your action becomes new training data!

This entire flow happens in < 200 milliseconds! 🚀

AI apps la two main data flow patterns irukku:

Most AI apps use BOTH! 🔥

• Batch: Daily model retraining, historical analysis
• Real-time: Live predictions, instant personalization

Example — Netflix:

• Batch: Nightly — retrain recommendation model with all users' watch history
• Real-time: While you browse — update suggestions based on what you just watched

Different AI apps la data flow eppadi vary aagudhu:

🚗 Self-Driving Car

• Sensors → Edge processing → Cloud upload → Model update → Back to car
• Latency critical — milliseconds matter!

💬 ChatGPT

• User prompt → Tokenization → Model inference → Token generation → Response
• Stateless per request, but conversation context maintained

🏦 Fraud Detection

• Transaction → Real-time scoring → Alert/Block → Human review → Model update
• False positives feedback loop crucial

🎵 Spotify Discover Weekly

• Weekly batch: All listening data → Feature extraction → Collaborative filtering → Playlist generated
• Monday morning delivery — pure batch flow

📸 Google Photos Search

• Photo upload → Image embedding generation → Vector storage → Search query → Vector similarity → Results
• Hybrid: Batch embedding + real-time search

Data flow la common ah varum problems:

⚠️ Data Lag — Real-time ah venum ana batch processing use pannudhu. Users get stale recommendations.

⚠️ Data Loss — Pipeline failure la data drop aaidum. Message queues (Kafka) use panni prevent pannanum.

⚠️ Data Skew — Some partitions la too much data, some la too little. Processing uneven aaidum.

⚠️ Schema Drift — Source data format maaridum, pipeline break aaidum. Schema registry use pannunga.

⚠️ Training-Serving Skew — Training la use panna features, serving la different ah irukkum. Feature store solves this!

⚠️ Feedback Delay — Model bad predictions pannudhu, but feedback late ah varudhu. Monitoring critical! 📊

Each stage ku popular tools:

Beginner ku: Pandas → SQLite → scikit-learn → FastAPI — simple stack la start pannunga! 🎯

Simple AI data flow oru project ah build pannunga:

Mini Project: Movie Recommendation Data Flow 🎬

Step 1: Data Collection

• Download MovieLens dataset (free, 100K ratings)

Step 2: Ingestion

• Python script la read pannunga

Step 3: Processing

• Remove users with < 5 ratings
• Normalize rating scales
• Handle missing values

Step 4: Storage

• SQLite database la store pannunga

Step 5: Feature Engineering

• User average rating, genre preferences, recency

Step 6: Model Training

• Simple collaborative filtering (surprise library)

Step 7: Serving

• FastAPI endpoint: /recommend?user_id=123

Step 8: Monitoring

• Log predictions, track accuracy over time

Total time: Weekend project! Perfect for learning end-to-end data flow. 💪

Summary:

✅ Data flow = Collection → Ingestion → Processing → Storage → Features → Training → Serving → Monitoring

✅ Biryani restaurant analogy — every stage matters for final taste

✅ Batch (scheduled) vs Real-time (continuous) — most apps use both

✅ Feature Store prevents training-serving skew

✅ Monitoring & feedback loop makes AI systems improve over time

✅ End-to-end understanding is key for data engineers

Next article: "What is ETL?" — data transformation oda core concept paapom! 🎯

Challenge: Build Oru Mini Recommendation Data Flow

Movie recommendation system complete data flow practice pannu:

Step 1 (Collect - 5 min):

• MovieLens free dataset download pannu (~1MB sample, 100K ratings)
• Columns: user_id, movie_id, rating, timestamp

Step 2 (Ingest & Process - 10 min):

Step 3 (Feature Engineering - 10 min):

Step 4 (Simple Model - 5 min):

• User-movie matrix create pannu
• Collaborative filtering basic (cosine similarity)

Step 5 (Serve - 5 min):

Result: Collection → Ingestion → Processing → Storage → Features → Model → Serving

Indhu complete end-to-end AI data flow! Real production la complex, but foundation same dhaan! 🎬

Q1: Data flow stages explain pannu?

A: Collection (raw data gather) → Ingestion (move to system) → Processing (clean & transform) → Storage (persist) → Feature eng (prepare for ML) → Training (model learn) → Serving (make predictions) → Monitoring (track performance). Each stage critical!

Q2: Batch vs Real-time data flow – recommendation system ku edhu choose pannuvanga?

A: Both! Netflix batch training nightly (retraining model with all user data). But real-time serving – nee browse panumbodhu live suggestions. Architecture: Batch training + Real-time serving = best practice.

Q3: Training-serving skew na enna? Feature engineering context la enna problem?

A: Training la feature value oru way compute pannum, serving la vera way – mismatch! Example: training la historical 30 days avg calculate pannum, serving la latest 5 days avg calculate pannu. Model accuracy training la 95%, production la 60%. Feature Store solve pannum – both places same features!

Q4: Data flow la bottleneck irukka eppadi identify pannum?

A: Monitoring! Each stage la latency track pannu. Collection→Ingestion slow? Source problem. Ingestion→Processing slow? Processing optimization venum. Storage la query slow? Indexing or partitioning. Metrics dashboard maintain panni bottleneck visible pannunga.

Q5: Payment transaction data flow design panna critical consideration enna?

A: Exactly-once processing – transaction duplicated aagakoodadhu! Idempotency implement pannunga. Audit trail maintain – compliance ka (GDPR, PCI DSS). Real-time processing – fraud instant detect. Failure handling – transaction neither lose aagakoodadhu nor duplicate aagakoodadhu. Architecture romba important!