Real-Time Streaming Forecasts for Nepal's Stock Market: MLOps and Generative AI at Scale
Yesterday I tried to run my batch‑oriented liquidity forecaster on a live NEPSE feed and watched the latency balloon as soon as the order book turn…
machine-learning
Optimizing for Speed and Persistence: Taming F1 Prediction with Optuna's Pruning and RDB Storage
Learn to implement an efficient, resumable, and robust hyperparameter optimization pipeline using Optuna's pruning mechanism and persistent s…
Stacking Ensembles: Building Robust Text Classifiers for Noisy API Data
Have you ever found yourself wrestling with text data from a public API, trying to build a classifier, only to realize that the 'clean' e…
Beyond Grid Search: Production-Grade Bayesian Optimization for ML Models with Optuna and F1 Laps
There's a moment when you realize that tweaking hyperparameters by hand, or even with brute-force Grid Search, just isn't cutting it anym…
Architecting Real-Time Inference: Deploying a Text Classifier with Asynchronous FastAPI
This post teaches how to build a performant, resilient, and asynchronous FastAPI microservice to serve a pre-trained machine learning model for rea…
Scaling Dynamic Recommendations: Semantic Embeddings and ANN for Live Content Feeds
Have you ever found yourself scrolling through an engineering blog, wishing it could magically suggest the next article you'd truly find insi…
Architecting Reproducibility: Tracking Dynamic ML Experiments and Explainability with MLflow
Have you ever found yourself in that all-too-familiar situation where a deployed machine learning model’s performance starts to drift, and you’re…
Architecting Explainability: Deconstructing Model Decisions with Permutation Importance and SHAP for Dynamic Text Data
Master the application of permutation importance for global feature relevance and SHAP waterfall plots for local, instance-level explanations, en…
Architecting Deep Insights: Hybrid PCA-t-SNE for Dynamic Text Stream Analysis
Master the strategic application and combination of PCA and t-SNE to transform high-dimensional text data from dynamic streams into meaningful lo…
Shrinking the Footprint, Boosting the Speed: Post-Training Quantization for Production Text Classifiers
Strategically applying post-training quantization can drastically reduce the memory footprint and inference latency of text classification models…