13. Feature Stores in Production (Feast): Real-Time Online Feature Retrieval vs Offline Batch Processing • AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) • Rishabh Ki Kaksha

AI Infrastructure, GPU Serving & MLOps (CUDA, Triton, TGI) › 13. Feature Stores in Production (Feast): Real-Time Online Feature Retrieval vs Offline Batch Processing

45 mins

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Course Syllabus

Module 1: GPU Architecture, CUDA Internals & Hardware Hierarchy

01. NVIDIA GPU Architecture: Streaming Multiprocessors (SMs), Tensor Cores, CUDA Cores & HBM3 Memory Bandwidth 02. GPU Memory Hierarchy: Registers, Shared Memory (SRAM), L1/L2 Caches & High-Bandwidth VRAM 03. CUDA Programming Basics: Writing Custom GPU Kernels in C++/CUDA & Triton Python DSL

Part 1: GPU Acceleration, Serving & Production Guardrails

Module 2: High-Performance LLM Serving Engines (vLLM, TGI, Triton)

04. The KV Cache Bottleneck: Memory Footprint Calculations & PagedAttention Virtual Memory Architecture 05. Dynamic & Continuous Batching (Iteration-Level Scheduling) vs Static Batching in LLM Serving 06. Production Benchmark: vLLM vs HuggingFace TGI (Text Generation Inference) vs NVIDIA Triton Inference Server 07. Speculative Decoding & Medusa Multi-Head Decoding for 2-3x Faster Token Generation

Module 3: Multi-GPU & Distributed Inference: Tensor & Pipeline Parallelism

08. Distributed AI Parallelism Paradigms: Data Parallelism (DP), Tensor Parallelism (TP), Pipeline Parallelism (PP) 09. Tensor Parallelism in Action: Megatron-LM Matrix Splitting across NVLink & NVSwitch Networks 10. DeepSpeed & PyTorch FSDP (Fully Sharded Data Parallel): Zero Redundancy Optimizer (ZeRO-1, ZeRO-2, ZeRO-3)

Module 4: MLOps Pipelines: Model Registry, Experiment Tracking (MLflow/W&B)

11. MLOps Foundations: Model Versioning, Artifact Registries & Lineage Tracking with MLflow and Weights & Biases 12. Automated Continuous Training Pipelines with Kubeflow Pipelines & GitHub Actions CI/CD for AI Models 13. Feature Stores in Production (Feast): Real-Time Online Feature Retrieval vs Offline Batch Processing

Module 5: Kubernetes GPU Orchestration, Auto-Scaling & Continuous Observability

14. Kubernetes for AI Workloads: NVIDIA GPU Operator, Device Plugins & Time-Slicing vs Multi-Instance GPU (MIG) 15. Horizontal Pod Auto-Scaling (HPA) for LLM Services based on TTFT (Time to First Token) & Queue Depth 16. Serverless GPU Architecture: Ray Serve, Modal & RunPod for Auto-Scaling AI APIs with Zero Cold Starts 17. Observability for GenAI: OpenLLMetry, Arize Phoenix, Langfuse & Prometheus GPU Utilization Metrics 18. Enterprise Cloud Cost Optimization: Spot GPU Instances, Multi-Cloud Orchestration & Slicing VRAM Costs

Interactive Lesson Studio

# 13. Feature Stores in Production (Feast): Real-Time Online Feature Retrieval vs Offline Batch Processing

> AI Infrastructure & MLOps Notes by Rishabh - Topic 13

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### 1. Training-Serving Skew (The ML Production Killer ⚠️)

Data scientists training ke liye Python script me feature calculate karte hain, jabki backend engineers production API me alag logic likhte hain $\rightarrow$ **Feature Inconsistency & Production Failures!**  
**Feature Store (Feast 👑):** Single unified feature definition jo:
*   **Offline Store (BigQuery / Snowflake):** Historical feature retrieval for model training.
*   **Online Store (Redis / DynamoDB):** Sub-millisecond ($< 2\text{ms}$) real-time feature lookup for live inference!

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### 2. Interview Ke 3 Sawal Is Topic Se

1. **Point-in-Time Correctness (Time-Travel Joins) training data me kyu zaroori hai?** -> Data leakage prevent karne ke liye taaki model training feature timestamp prediction time se pehle ka hi record utilize kare.
2. **Online Feature Store me Redis cluster kyu use hota hai?** -> Microsecond key-value entity feature lookup latency deliver karne ke liye.
3. **Streaming Feature ingestion (Kafka to Feature Store) kaise kaam karta hai?** -> Flink / Spark Streaming se clickstream events process karke live user features Redis me incrementally update karna.

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**Next Topic:** 14. Kubernetes for AI Workloads: NVIDIA GPU Operator, Device Plugins & Time-Slicing vs Multi-Instance GPU (MIG)

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