What is Feature store?Feature store kya hai?

🧸 Think of it likeAise samjho

Like a shared pantry of ready made masalas, so every cook uses exactly the same mix in training and in the real kitchen.Ready made masalon ki shared pantry jaisa, taaki har cook training aur asli kitchen dono mein bilkul same mix use kare.

📘 ExplainedVistaar se

Teams compute features once and reuse them, with point in time correctness for training (no peeking into the future) and low latency lookup for serving. This prevents training serving skew. Examples include Feast and the feature stores in cloud ML platforms.Teams features ek baar banati hain aur dobara use karti hain, training ke liye point in time sahi (future mein jhaankna nahi) aur serving ke liye low latency lookup ke saath. Isse training serving skew rukta hai. Examples: Feast aur cloud ML platforms ke feature stores.

💡 Real exampleAsli example

Fraud scoring reads 'transactions in the last 10 minutes' from the feature store in milliseconds.Fraud scoring feature store se milliseconds mein 'pichhle 10 minute ki transactions' padhta hai.

📖 Learn it step by stepStep by step seekho

• Training vs inference: how ML runs in productionTraining vs inference: ML production mein kaise chalta hai
• Streaming data: Kafka and real-time pipelinesStreaming data: Kafka aur real-time pipelines

🔗 Related termsJude hue shabd

• FeatureOne input the model uses to make a prediction, like age, income or number of logins.Ek input jo model prediction ke liye use karta hai, jaise umar, income ya logins ki ginti.
• Training serving skewWhen features are computed differently in training and in production, quietly hurting the model.Jab training aur production mein features alag tarah se bante hain, aur model chupke se kharab hota hai.