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
50
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 17:25:37 | V1011 | 10.9212% | 10.9212% ↑ | 1.1186% | 1.1186% ↑ | 9.9% | 2,746 | Skip |
| 2026-04-16 17:25:35 | V1003 | 6.2799% | 6.2429% ↓ | 1.0163% | 1.0200% ↑ | 24.1% | 2,746 | ✅ Deploy |
| 2026-04-16 17:25:26 | V1009 | 7.4151% | 7.3402% ↓ | 0.8300% | 0.8331% ↑ | 16.7% | 2,746 | ✅ Deploy |
| 2026-04-16 17:25:25 | V1001 | 5.1907% | 5.3652% ↑ | 1.4930% | 1.7251% ↑ | 21.4% | 2,746 | Skip |
| 2026-04-16 14:25:23 | V1011 | 10.9490% | 10.9212% ↓ | 1.1204% | 1.1186% ↓ | 11.4% | 2,710 | ✅ Deploy |
| 2026-04-16 14:25:15 | V1003 | 6.1186% | 6.0712% ↓ | 1.0102% | 1.0094% ↓ | 26.9% | 2,710 | ✅ Deploy |
| 2026-04-16 14:25:06 | V1009 | 7.2033% | 7.1765% ↓ | 0.8226% | 0.8202% ↓ | 36.4% | 2,709 | ✅ Deploy |
| 2026-04-16 14:24:57 | V1001 | 5.1938% | 5.3506% ↑ | 1.4909% | 1.4529% ↓ | 22.9% | 2,709 | ✅ Deploy |
| 2026-04-16 11:24:54 | V1011 | 10.7969% | 10.7791% ↓ | 1.0972% | 1.1137% ↑ | 21.6% | 2,673 | ✅ Deploy |
| 2026-04-16 11:24:46 | V1003 | 6.2564% | 6.2289% ↓ | 0.9517% | 0.9555% ↑ | 20.9% | 2,673 | ✅ Deploy |
| 2026-04-16 11:24:38 | V1009 | 7.0818% | 7.0369% ↓ | 0.8275% | 0.8217% ↓ | 10.2% | 2,673 | ✅ Deploy |
| 2026-04-16 11:24:37 | V1001 | 5.1644% | 5.1944% ↑ | 1.4954% | 1.7475% ↑ | 21.9% | 2,673 | Skip |
| 2026-04-16 08:24:34 | V1011 | 10.8450% | 10.7792% ↓ | 1.1366% | 1.1268% ↓ | 34.0% | 2,637 | ✅ Deploy |
| 2026-04-16 08:24:33 | V1003 | 5.9309% | 5.9350% ↑ | 0.9334% | 0.9368% ↑ | 17.3% | 2,637 | Skip |
| 2026-04-16 08:24:24 | V1009 | 6.8245% | 6.7874% ↓ | 0.8164% | 0.8164% ↓ | 15.4% | 2,637 | ✅ Deploy |
| 2026-04-16 08:24:16 | V1001 | 5.1884% | 5.2340% ↑ | 1.5397% | 1.4872% ↓ | 25.0% | 2,637 | ✅ Deploy |
| 2026-04-16 05:24:15 | V1011 | 10.7487% | 10.7742% ↑ | 1.0448% | 1.0647% ↑ | 47.3% | 2,601 | Skip |
| 2026-04-16 05:24:14 | V1003 | 5.5655% | 5.6644% ↑ | 0.9411% | 0.9487% ↑ | 19.1% | 2,601 | Skip |
| 2026-04-16 05:24:04 | V1009 | 6.4771% | 6.4671% ↓ | 0.8050% | 0.8087% ↑ | 18.2% | 2,601 | ✅ Deploy |
| 2026-04-16 05:24:03 | V1001 | 5.1517% | 5.1529% ↑ | 1.5154% | 1.6811% ↑ | 13.1% | 2,601 | Skip |
| 2026-04-16 02:24:02 | V1011 | 10.7126% | 10.8086% ↑ | 1.0081% | 1.0179% ↑ | 10.2% | 2,565 | Skip |
| 2026-04-16 02:24:01 | V1003 | 5.3220% | 5.4896% ↑ | 0.9502% | 0.9557% ↑ | 31.9% | 2,565 | Skip |
| 2026-04-16 02:24:00 | V1009 | 6.2978% | 6.5689% ↑ | 0.7547% | 0.7633% ↑ | 25.0% | 2,565 | Skip |
| 2026-04-16 02:23:59 | V1001 | 5.0650% | 5.0928% ↑ | 1.4791% | 1.7081% ↑ | 1.0% | 2,565 | Skip |
| 2026-04-15 23:23:58 | V1011 | 10.1252% | 10.1252% ↑ | 0.9977% | 0.9977% ↑ | 5.3% | 2,529 | Skip |
| 2026-04-15 23:23:56 | V1003 | 5.1647% | 5.6499% ↑ | 0.9404% | 0.9336% ↓ | 21.7% | 2,529 | ✅ Deploy |
| 2026-04-15 23:23:55 | V1009 | 6.0007% | 6.2773% ↑ | 0.7333% | 0.7366% ↑ | 27.1% | 2,529 | Skip |
| 2026-04-15 23:23:46 | V1001 | 5.1035% | 5.0043% ↓ | 1.3918% | 1.6033% ↑ | 11.4% | 2,529 | ✅ Deploy |
| 2026-04-15 20:23:46 | V1011 | 10.1252% | 10.6533% ↑ | 0.9977% | 1.0444% ↑ | 25.4% | 2,493 | Skip |
| 2026-04-15 20:23:45 | V1003 | 4.7793% | 5.4595% ↑ | 0.9284% | 0.9690% ↑ | 42.3% | 2,493 | Skip |
| 2026-04-15 20:23:35 | V1009 | 5.5876% | 6.0565% ↑ | 0.7119% | 0.6987% ↓ | 39.7% | 2,493 | ✅ Deploy |
| 2026-04-15 20:23:35 | V1001 | 4.9209% | 4.9319% ↑ | 1.3732% | 1.7545% ↑ | 2.3% | 2,493 | Skip |
| 2026-04-15 17:23:34 | V1011 | 9.3895% | 9.3895% ↑ | 0.9890% | 0.9890% ↑ | 9.9% | 2,457 | Skip |
| 2026-04-15 17:23:34 | V1003 | 4.5281% | 5.4035% ↑ | 0.8922% | 0.9497% ↑ | 52.3% | 2,457 | Skip |
| 2026-04-15 17:23:33 | V1009 | 5.3192% | 6.0093% ↑ | 0.7051% | 0.7207% ↑ | 39.4% | 2,457 | Skip |
| 2026-04-15 17:23:31 | V1001 | 4.5102% | 4.5102% ↑ | 1.2083% | 1.2083% ↑ | 2.6% | 2,457 | Skip |
| 2026-04-15 14:23:30 | V1011 | 9.3895% | 10.1705% ↑ | 0.9890% | 1.1037% ↑ | 27.0% | 2,421 | Skip |
| 2026-04-15 14:23:30 | V1003 | 4.3167% | 5.4168% ↑ | 0.8226% | 0.9118% ↑ | 47.9% | 2,421 | Skip |
| 2026-04-15 14:23:29 | V1009 | 4.9343% | 5.8526% ↑ | 0.7077% | 0.7723% ↑ | 46.9% | 2,421 | Skip |
| 2026-04-15 14:23:28 | V1001 | 4.5102% | 4.7181% ↑ | 1.2083% | 1.8723% ↑ | 7.9% | 2,421 | Skip |
| 2026-04-15 11:23:27 | V1011 | 8.9134% | 9.8771% ↑ | 0.8942% | 1.0098% ↑ | 11.5% | 2,385 | Skip |
| 2026-04-15 11:23:27 | V1003 | 4.1888% | 5.8651% ↑ | 0.7780% | 0.8882% ↑ | 36.2% | 2,385 | Skip |
| 2026-04-15 11:23:26 | V1009 | 4.7017% | 5.7113% ↑ | 0.6991% | 0.7918% ↑ | 39.1% | 2,385 | Skip |
| 2026-04-15 11:23:25 | V1001 | 4.2803% | 4.5501% ↑ | 1.1745% | 1.5589% ↑ | 0.8% | 2,385 | Skip |
| 2026-04-15 08:23:24 | V1011 | 7.9000% | 7.9000% ↑ | 0.8005% | 0.8005% ↑ | 6.1% | 2,349 | Skip |
| 2026-04-15 08:23:24 | V1003 | 4.0203% | 6.1929% ↑ | 0.7525% | 0.8881% ↑ | 50.3% | 2,349 | Skip |
| 2026-04-15 08:23:23 | V1009 | 4.4516% | 5.8120% ↑ | 0.6994% | 0.8733% ↑ | 37.8% | 2,349 | Skip |
| 2026-04-15 08:23:22 | V1001 | 4.1388% | 4.5696% ↑ | 1.1271% | 1.6988% ↑ | 8.2% | 2,349 | Skip |
| 2026-04-15 05:23:21 | V1011 | 7.9000% | 9.1244% ↑ | 0.8005% | 0.9303% ↑ | 41.6% | 2,313 | Skip |
| 2026-04-15 05:23:20 | V1003 | 3.9943% | 6.5205% ↑ | 0.7336% | 0.8867% ↑ | 42.0% | 2,313 | 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