Online Learning & Drift Detection
প্রতি ৩ ঘণ্টায় স্বয়ংক্রিয় model retrain · উন্নত হলেই deploy
কৌশল
Frequency
৩ ঘণ্টা
Auto retrain
Drift Threshold
১০%
CPU বা RAM mean
Deploy Policy
উন্নত হলে
Only improved
মোট Retrain
18
Online Learning এর প্রভাব — প্রমাণিত ফলাফল
CPU Forecast
আগে
৮.২০%
RMSE
→
পরে
২.৫৯%
RMSE
৬৮%
উন্নত
R²: ০.৫১১ → ০.৯৫১ · CPU Drift: ৮১.৮৩%
RAM Forecast
আগে
১.০৪%
RMSE
→
পরে
০.৫৯%
RMSE
৪৩%
উন্নত
R²: ০.৬৫২ → ০.৮৮৯ · RAM Drift: ০.৩৪%
RMSE উন্নতির গ্রাফ
Retrain ইতিহাস
| সময় | VPS | CPU আগে | CPU পরে | RAM আগে | RAM পরে | CPU Drift | Samples | Status |
|---|---|---|---|---|---|---|---|---|
| 2026-04-16 17:25:26 | V1009 | 7.4151% | 7.3402% ↓ | 0.8300% | 0.8331% ↑ | 16.7% | 2,746 | ✅ Deploy |
| 2026-04-16 14:25:06 | V1009 | 7.2033% | 7.1765% ↓ | 0.8226% | 0.8202% ↓ | 36.4% | 2,709 | ✅ Deploy |
| 2026-04-16 11:24:38 | V1009 | 7.0818% | 7.0369% ↓ | 0.8275% | 0.8217% ↓ | 10.2% | 2,673 | ✅ Deploy |
| 2026-04-16 08:24:24 | V1009 | 6.8245% | 6.7874% ↓ | 0.8164% | 0.8164% ↓ | 15.4% | 2,637 | ✅ Deploy |
| 2026-04-16 05:24:04 | V1009 | 6.4771% | 6.4671% ↓ | 0.8050% | 0.8087% ↑ | 18.2% | 2,601 | ✅ Deploy |
| 2026-04-16 02:24:00 | V1009 | 6.2978% | 6.5689% ↑ | 0.7547% | 0.7633% ↑ | 25.0% | 2,565 | Skip |
| 2026-04-15 23:23:55 | V1009 | 6.0007% | 6.2773% ↑ | 0.7333% | 0.7366% ↑ | 27.1% | 2,529 | Skip |
| 2026-04-15 20:23:35 | V1009 | 5.5876% | 6.0565% ↑ | 0.7119% | 0.6987% ↓ | 39.7% | 2,493 | ✅ Deploy |
| 2026-04-15 17:23:33 | V1009 | 5.3192% | 6.0093% ↑ | 0.7051% | 0.7207% ↑ | 39.4% | 2,457 | Skip |
| 2026-04-15 14:23:29 | V1009 | 4.9343% | 5.8526% ↑ | 0.7077% | 0.7723% ↑ | 46.9% | 2,421 | Skip |
| 2026-04-15 11:23:26 | V1009 | 4.7017% | 5.7113% ↑ | 0.6991% | 0.7918% ↑ | 39.1% | 2,385 | Skip |
| 2026-04-15 08:23:23 | V1009 | 4.4516% | 5.8120% ↑ | 0.6994% | 0.8733% ↑ | 37.8% | 2,349 | Skip |
| 2026-04-15 05:23:19 | V1009 | 4.0221% | 5.8804% ↑ | 0.7367% | 1.1015% ↑ | 42.2% | 2,313 | Skip |
| 2026-04-15 02:23:17 | V1009 | 3.7602% | 6.1353% ↑ | 0.7291% | 1.1007% ↑ | 43.0% | 2,277 | Skip |
| 2026-04-14 23:23:14 | V1009 | 3.6929% | 6.1868% ↑ | 0.7241% | 1.1171% ↑ | 49.8% | 2,241 | Skip |
| 2026-04-14 23:15:03 | V1009 | 3.6958% | 6.3689% ↑ | 0.7238% | 1.1171% ↑ | 50.4% | 2,239 | Skip |
| 2026-04-14 21:40:49 | V1009 | 3.4615% | 6.5787% ↑ | 0.6788% | 1.1058% ↑ | 58.7% | 2,221 | Skip |
| 2026-04-14 20:38:14 | V1009 | 3.4788% | 6.6989% ↑ | 0.6337% | 1.0879% ↑ | 54.8% | 2,208 | Skip |
কীভাবে কাজ করে
📊
সংগ্রহ
প্রতি ৩০ সেকেন্ডে নতুন data
📈
Drift শনাক্ত
নতুন vs পুরনো mean তুলনা
🔄
Retrain
XGBoost পুনরায় train
✅
যাচাই
পুরনো vs নতুন RMSE
🚀
Deploy
উন্নত হলেই replace