Tag: Machine Learning
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W&B Artifacts and Team Collaboration: Model Version Control
W&B Artifacts version models like Git commits. Real lineage tracking, team workflows, and the one metadata trick that saved a 3AM rollback.
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ML Experiment Tracking with W&B: Lessons from 50 Failed Runs
W&B cuts experiment chaos in half โ but only if you log the right metrics. Here's what 50 runs taught me.
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Stock Time Series: Why ARIMA Failed and LSTM Barely Worked
ARIMA just repeats yesterday's price. LSTM hits 52% directional accuracy. Why stock forecasting tutorials hide the real success rates.
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Stable Baselines3: Tips for Training Robust RL Agents
Train production-ready RL agents with Stable Baselines3: hyperparameter tuning tricks, TensorBoard monitoring, and debugging NaN rewards fast.
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Deploying RUL Models: Optimization and PHM Integration
Deploy RUL models without crashing production: quantization cuts inference from 45ms to 8ms. Real metrics, optimization code, and PHM integration.
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RUL Prediction in Python: Linear Regression to LSTM
Build bearing RUL prediction in Python: linear regression baseline to LSTM with NASA C-MAPSS data. Full code, degradation features, performance.
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ML Stock Prediction: Why Most Fail and What Works
Most ML stock models fail live trading within weeks. Here's what survives transaction costs, regime shifts, and the reality of slippage.
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Predictive Maintenance 101: ML to Prevent Downtime
Predict machine failures 72 hours early using survival analysis and vibration FFT features. Why threshold alerts miss 60% of breakdowns.