Supervised Learning for Churn: A Beginner's Guide
Guide to building supervised churn models: collect and clean data, engineer features, train Logistic/RandomForest/XGBoost, and evaluate with recall and F1.
NanoGPT Blog
Updates, guides, and insights from the NanoGPT team
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How to Detect and Block Malicious Bots in APIs
Layer IP reputation, behavioral analysis, rate limits, TLS fingerprints, and CAPTCHAs to detect and block malicious bots targeting your APIs.
AI Model Testing Protocols: Best Practices
Practical guidelines for testing AI models: define objectives, build golden datasets, run edge-case and adversarial tests, version control, and monitor drift.
Ultimate Guide to AI Model Storage Needs
Practical guide to AI storage: VRAM/RAM sizing, NVMe vs HDD, checkpoints, object storage, and caching strategies to prevent GPU stalls and cut costs.
AI Infrastructure: Redundancy vs. High Availability
Compare redundancy and high availability for AI infrastructure — tradeoffs in cost, recovery time, and how combining them improves resilience.
Latency Profiling for Large Language Models
Guide to profiling LLM latency: measure TTFT, TPOT, and ITL; use PyTorch, Nsight, and tracing; optimize batching, quantization, and memory bandwidth.
Dynamic Load Balancing: Role of Round Robin Algorithms
Compare Round Robin, Weighted and Dynamic methods, their trade-offs, and the best use cases for web, cloud, and AI workload balancing.
How to Monitor AI Models for Errors
Monitor AI models to catch silent failures—track hallucinations, data drift, latency, token costs, set alerts, and automate retraining.
RNN Variants: Performance Comparison
Compare Vanilla RNNs, LSTMs, and GRUs—memory, speed, parameter trade-offs and best use cases for short, medium, and long sequence tasks.
5 Partitioning Methods for Multi-GPU Training
Compare five multi-GPU partitioning strategies—data, model, pipeline, sharded, and fully sharded—to balance memory, communication, and scalability.