Designs

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All designs 10

Ordered by design number

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104. Design a Feature Store
Define a feature once, train on point-in-time correct history and serve 200 fresh values in under 10 ms.
ClassicMedium
Streaming Machine learning AI infrastructure
+2
AWS Features
Worked design
Practice
105. Design a Distributed Training Platform
Train 1 B to 400 B models on 8 to 10,240 H100s: DDP, ZeRO and FSDP, tensor and pipeline parallel over NVLink and EFA, checkpoints, hot spares, MFU and goodput.
ClassicMedium
Machine learning AI infrastructure AWS
+3
GPUs Parallelism Training
Worked design
Practice
107. Design an ML Model Serving and Deployment Platform
From registry to traffic for hundreds of models: gates, shadow and canary with automatic rollback, packed CPU and GPU pools, LLM serving, cells and audit.
ClassicMedium
AI infrastructure AWS Deployment
+2
MLOps Serving
Worked design
Practice
111. Design Checkpointing for a 10,000-GPU Training Run
Save the 1 TB state of a 70 B model on 10,000 GPUs often enough that a failure costs minutes, without stalling training, and restore fast.
ClassicMedium
Machine learning AI infrastructure AWS
+4
Checkpointing Recovery Storage Training
Worked design
Practice
112. Design ML Model Monitoring and Drift Detection
Know within hours when hundreds of production models see broken inputs, drift or falling quality, before the labels arrive.
ClassicMedium
Observability AI infrastructure AWS
+3
Drift MLOps Monitoring
Worked design
Practice
113. Design a Data Labeling Platform
Route images, text, audio and model replies to thousands of annotators, buy quality with gold and consensus, cut cost with models, and ship versioned datasets.
ClassicMedium
Machine learning AI infrastructure AWS
+2
Human in the loop Labeling
Worked design
Practice
116. Design an LLM Pretraining Pipeline
Pretraining a base LLM on a fixed budget: sizing by 6ND and scaling laws, the data factory, tokenizer, stable runs, failures and evals.
ClassicMedium
Machine learning Generative AI AI infrastructure
+3
AWS LLMs Training
Worked design
Practice
121. Design an LLM Evaluation Platform
GPU batch generation behind a shared cache, sandboxed pass@k, calibrated judges, paired statistics, release gates, contamination checks.
ClassicMedium
Generative AI AI infrastructure AWS
+3
Evaluation LLMs MLOps
Worked design
Practice
122. Design an Experiment Tracking and Model Registry Service
Experiment tracking and a model registry: non-blocking logging, chunked metric curves, content-addressed artifacts, lineage and a promotion gate. One-hour boards for junior, senior and staff, with the theory behind them.
ClassicMedium
Machine learning AI infrastructure AWS
+3
Experiment tracking MLOps Model registry
Worked design
Practice
123. Design a Hyperparameter Tuning Service
A tuning service like Vizier: random and Bayesian search, ASHA and population-based training, trials on a shared GPU cluster.
ClassicMedium
AI infrastructure AWS Hyperparameter tuning
+2
MLOps Optimization
Worked design
Practice

Each design brings together the brief, practice, examples and discussion.

All designs 10

104. Design a Feature Store

105. Design a Distributed Training Platform

107. Design an ML Model Serving and Deployment Platform

111. Design Checkpointing for a 10,000-GPU Training Run

112. Design ML Model Monitoring and Drift Detection

113. Design a Data Labeling Platform

116. Design an LLM Pretraining Pipeline

121. Design an LLM Evaluation Platform

122. Design an Experiment Tracking and Model Registry Service

123. Design a Hyperparameter Tuning Service