AI & MLOps System Designs
Worked AI & MLOps system design walkthroughs: the requirements, the trade-offs, real numbers and the component choices behind each design.
A/B Testing and Online Experimentation Platform
End-to-end system for running statistically rigorous A/B tests at scale: assignment, event collection, metric computation, and decision support for ML and product teams.
AI & MLOps · intermediate · 12 min read
AI Agent Platform: Tool Calling, Memory, Sandboxing, and Step Budgets
End-to-end design of a production AI agent platform covering tool dispatch, multi-tier memory, secure sandboxed execution, and deterministic step-budget enforcement.
AI & MLOps · advanced · 12 min read
CI/CD for Machine Learning: Reproducible Builds, Automated Retraining and Model Promotion
End-to-end pipeline design for training, validating, and promoting ML models with the same rigor as software releases—covering reproducibility, automated gates, and safe rollout.
AI & MLOps · intermediate · 12 min read
Distributed Model Training Pipeline: Sharding, Checkpointing and Fault Recovery
End-to-end design of a fault-tolerant distributed training system covering data and model parallelism, checkpoint coordination, and automatic worker recovery at scale.
AI & MLOps · advanced · 12 min read
Embedding Pipeline: Bulk Generation, Incremental Refresh, and Reindexing
End-to-end design for generating, storing, and refreshing vector embeddings at scale — covering bulk ingestion, change-driven updates, and zero-downtime reindexing.
AI & MLOps · intermediate · 12 min read
Experiment Tracking and the Model Registry: Runs, Artefacts, Lineage and Promotion
A deep-dive into how ML platforms record experiments, store artefacts, track lineage, and gate model promotion from dev to production.
AI & MLOps · intermediate · 12 min read
Feature Store: Offline/Online Parity, Point-in-Time Correctness, and Backfills
Design a production-grade feature store that serves consistent features for training and inference, enforces point-in-time correctness, and supports large-scale backfills without data leakage.
AI & MLOps · advanced · 12 min read
Fine-Tuning Platform: LoRA Adapters, Dataset Curation and Evaluation Gates
End-to-end system design for a production fine-tuning platform covering LoRA adapter training, dataset versioning, automated evaluation gates, and serving infrastructure.
AI & MLOps · advanced · 12 min read
GPU Cluster Scheduling: Queues, Gang Scheduling, Fractional GPUs, and Multi-Tenancy
A deep-dive into how modern ML platforms schedule GPU workloads across shared clusters, covering queue design, gang scheduling, fractional GPU allocation, and fair multi-tenant isolation.
AI & MLOps · advanced · 12 min read
LLM Gateway: Multi-Provider Routing, Quotas, Cost Control and Failover
Design a production-grade LLM gateway that routes requests across OpenAI, Anthropic, and Cohere with per-tenant quotas, cost tracking, and automatic failover.
AI & MLOps · intermediate · 12 min read
LLM Guardrails: Moderation, Prompt Injection Defence, PII Redaction and Output Validation
A production architecture for wrapping LLM calls with layered safety controls: input moderation, injection detection, PII scrubbing, and structured output validation.
AI & MLOps · intermediate · 12 min read
LLM Inference Service: KV Cache, Continuous Batching, and Paged Attention
Design a production-grade LLM inference service handling thousands of concurrent requests using continuous batching, paged attention, and distributed KV caching to maximize GPU utilization.
AI & MLOps · advanced · 12 min read
LLM Observability and Evaluation: Tracing, Offline Evals, Online Scoring, and Regression Gates
End-to-end system for monitoring LLM applications in production: distributed tracing, automated offline evaluation pipelines, real-time quality scoring, and regression gates that block bad model rollouts.
AI & MLOps · intermediate · 12 min read
Model Monitoring: Data Drift, Concept Drift, Delayed Labels and Performance Decay
A production ML monitoring system that detects data drift, concept drift, and performance decay in real time, handling delayed ground-truth labels with statistical rigor.
AI & MLOps · intermediate · 12 min read
Model Rollout Strategies: Shadow Scoring, Canary, Champion-Challenger and Rollback
A deep-dive into production ML model rollout patterns—shadow scoring, canary releases, champion-challenger splits, and automated rollback—with concrete architecture, data models, and trade-offs.
AI & MLOps · intermediate · 12 min read
RDMA-Backed Storage for AI Training Clusters
Design a high-throughput, low-latency storage fabric that keeps GPU clusters fed during large-scale AI training using GPUDirect Storage, NVMe-oF, RoCE/InfiniBand, and parallel filesystems.
AI & MLOps · advanced · 12 min read
Real-Time Model Serving Platform: Batching, Autoscaling, and GPU Packing
End-to-end design of a production ML inference platform handling 50k RPS with sub-100ms p99 latency via dynamic batching, GPU bin-packing, and reactive autoscaling.
AI & MLOps · advanced · 12 min read
Retrieval-Augmented Generation: Chunking, Retrieval, Reranking, and Grounding
End-to-end design of a production RAG system covering document ingestion, vector search, cross-encoder reranking, and grounded LLM generation with citation tracking.
AI & MLOps · intermediate · 12 min read
Semantic Caching for LLM Responses
Cut LLM API costs 40–70% by caching responses keyed on embedding similarity rather than exact text match, with tunable thresholds, TTL-based invalidation, and safe degradation.
AI & MLOps · intermediate · 12 min read
Training Data Management: Labelling, Dataset Versioning and Data Quality Gates
End-to-end system for managing ML training data: human and automated labelling pipelines, immutable dataset versioning, and quality gates that block bad data before model training.
AI & MLOps · intermediate · 12 min read
Vector Database Internals: HNSW, IVF, Filtering, Sharding and Recall Tuning
A deep dive into building a production vector database: index structures, approximate nearest-neighbor search, metadata filtering, horizontal sharding, and recall/latency trade-offs.
AI & MLOps · advanced · 12 min read