Online Learning & Drift Detection
প্রতি ৩ ঘণ্টায় স্বয়ংক্রিয় model retrain · উন্নত হলেই deploy
Retraining Strategy
Frequency
৩ ঘণ্টা
Auto scheduled
Drift Threshold
১০%
CPU বা RAM mean drift
Deploy Policy
উন্নত হলে
Only if RMSE improves
মোট Retrain
50
এখন পর্যন্ত
🚀 সফল Deploy
7
মোট 50 retrain এর মধ্যে
📉 সর্বশেষ CPU RMSE
5.29%
আগে ছিল: 5.12%
⚡ সর্বশেষ CPU Drift
23.8%
Threshold: 10% · V1011
সর্বশেষ Retrain এর প্রভাব — Real-time Database থেকে
🧠 CPU Forecast — V1011 · 2026-10-12 20:19
আগে
5.12%
RMSE
→
পরে
5.29%
RMSE
3% ↑
খারাপ
Drift: 23.8%
· R²: 0.832 → 0.820
· Samples: 45,125
💾 RAM Forecast — V1011 · 2026-10-12 20:19
আগে
1.68%
RMSE
→
পরে
1.67%
RMSE
0% ↓
উন্নত
RAM Drift: 23.4%
· Deploy: ✅ হয়েছে
RMSE উন্নতির গ্রাফ
CPU RMSE (%)
RAM RMSE (%)
Retrain ইতিহাস (সর্বশেষ 50টি)
| সময় | VPS | CPU আগে | CPU পরে | RAM আগে | RAM পরে | CPU Drift | Samples | Status |
|---|---|---|---|---|---|---|---|---|
| 2026-10-12 20:19 | V1011 | 5.1203% | 5.2923% ↑ | 1.6770% | 1.6743% ↓ | 23.8% | 45,125 | ✅ Deploy |
| 2026-10-12 20:19 | V1001 | 6.1260% | 6.1334% ↑ | 2.8021% | 2.8453% ↑ | 22.0% | 44,567 | ⏭️ Skip |
| 2026-10-12 20:19 | V1003 | 7.2475% | 7.4264% ↑ | 1.0173% | 1.0169% ↓ | 290.6% | 45,125 | ✅ Deploy |
| 2026-10-12 20:18 | V1009 | 3.6816% | 3.6848% ↑ | 2.9175% | 3.0120% ↑ | 17.2% | 44,987 | ⏭️ Skip |
| 2026-10-12 17:18 | V1011 | 5.1206% | 5.1632% ↑ | 1.6771% | 1.6816% ↑ | 14.3% | 45,089 | ⏭️ Skip |
| 2026-10-12 17:18 | V1001 | 6.1287% | 6.1349% ↑ | 2.8005% | 2.8407% ↑ | 6.0% | 44,531 | ⏭️ Skip |
| 2026-10-12 17:18 | V1003 | 7.1658% | 7.2748% ↑ | 1.0177% | 1.0201% ↑ | 281.5% | 45,089 | ⏭️ Skip |
| 2026-10-12 17:18 | V1009 | 3.6796% | 3.6818% ↑ | 2.8967% | 3.0516% ↑ | 23.2% | 44,951 | ⏭️ Skip |
| 2026-10-12 14:18 | V1011 | 5.1148% | 5.1844% ↑ | 1.6800% | 1.6773% ↓ | 47.4% | 45,053 | ✅ Deploy |
| 2026-10-12 14:18 | V1001 | 6.1260% | 6.1308% ↑ | 2.8014% | 2.8430% ↑ | 33.5% | 44,495 | ⏭️ Skip |
| 2026-10-12 14:18 | V1003 | 7.0954% | 7.2485% ↑ | 1.0178% | 1.0201% ↑ | 294.6% | 45,053 | ⏭️ Skip |
| 2026-10-12 14:18 | V1009 | 3.6802% | 3.6835% ↑ | 2.8870% | 2.9949% ↑ | 18.8% | 44,915 | ⏭️ Skip |
| 2026-10-12 11:17 | V1011 | 5.1106% | 5.2218% ↑ | 1.6746% | 1.6858% ↑ | 43.5% | 45,017 | ⏭️ Skip |
| 2026-10-12 11:17 | V1001 | 6.1290% | 6.1312% ↑ | 2.7992% | 2.8407% ↑ | 43.5% | 44,459 | ⏭️ Skip |
| 2026-10-12 11:17 | V1003 | 6.9980% | 7.2063% ↑ | 1.0208% | 1.0242% ↑ | 276.8% | 45,017 | ⏭️ Skip |
| 2026-10-12 11:17 | V1009 | 3.6770% | 3.6806% ↑ | 2.8704% | 2.9596% ↑ | 25.2% | 44,879 | ⏭️ Skip |
| 2026-10-12 08:17 | V1011 | 5.1108% | 5.1634% ↑ | 1.6752% | 1.6846% ↑ | 55.8% | 44,981 | ⏭️ Skip |
| 2026-10-12 08:17 | V1001 | 6.1434% | 6.1482% ↑ | 2.8004% | 2.8417% ↑ | 38.7% | 44,423 | ⏭️ Skip |
| 2026-10-12 08:17 | V1003 | 6.9079% | 6.9655% ↑ | 1.0206% | 1.0234% ↑ | 254.9% | 44,981 | ⏭️ Skip |
| 2026-10-12 08:17 | V1009 | 3.6775% | 3.6812% ↑ | 2.8539% | 3.0207% ↑ | 18.9% | 44,843 | ⏭️ Skip |
| 2026-10-12 05:16 | V1011 | 5.1081% | 5.1810% ↑ | 1.6581% | 1.6685% ↑ | 61.6% | 44,945 | ⏭️ Skip |
| 2026-10-12 05:16 | V1001 | 6.1493% | 6.1582% ↑ | 2.8039% | 2.8452% ↑ | 44.5% | 44,387 | ⏭️ Skip |
| 2026-10-12 05:16 | V1003 | 6.8495% | 6.9248% ↑ | 1.0198% | 1.0236% ↑ | 272.9% | 44,945 | ⏭️ Skip |
| 2026-10-12 05:16 | V1009 | 3.6732% | 3.6758% ↑ | 2.8364% | 2.9555% ↑ | 33.6% | 44,807 | ⏭️ Skip |
| 2026-10-12 02:16 | V1011 | 5.1087% | 5.2138% ↑ | 1.6642% | 1.6587% ↓ | 51.6% | 44,909 | ✅ Deploy |
| 2026-10-12 02:16 | V1001 | 6.1538% | 6.1626% ↑ | 2.8131% | 2.8529% ↑ | 26.7% | 44,351 | ⏭️ Skip |
| 2026-10-12 02:16 | V1003 | 6.7872% | 6.9113% ↑ | 1.0201% | 1.0225% ↑ | 235.1% | 44,909 | ⏭️ Skip |
| 2026-10-12 02:15 | V1009 | 3.6836% | 3.6867% ↑ | 2.8230% | 2.9457% ↑ | 27.7% | 44,771 | ⏭️ Skip |
| 2026-10-11 23:15 | V1011 | 5.1079% | 5.1866% ↑ | 1.6650% | 1.6716% ↑ | 49.1% | 44,873 | ⏭️ Skip |
| 2026-10-11 23:15 | V1001 | 6.1670% | 6.1670% ↑ | 2.8112% | 2.8112% ↑ | 1.6% | 44,315 | ⏭️ Skip |
| 2026-10-11 23:15 | V1003 | 6.7310% | 6.8357% ↑ | 1.0153% | 1.0197% ↑ | 277.3% | 44,873 | ⏭️ Skip |
| 2026-10-11 23:15 | V1009 | 3.6841% | 3.6873% ↑ | 2.8086% | 2.9502% ↑ | 6.2% | 44,735 | ⏭️ Skip |
| 2026-10-11 20:15 | V1011 | 5.1052% | 5.2040% ↑ | 1.6604% | 1.6704% ↑ | 44.6% | 44,837 | ⏭️ Skip |
| 2026-10-11 20:15 | V1001 | 6.1670% | 6.1670% ↑ | 2.8112% | 2.8112% ↑ | 6.9% | 44,279 | ⏭️ Skip |
| 2026-10-11 20:15 | V1003 | 6.6646% | 6.7139% ↑ | 1.0141% | 1.0173% ↑ | 236.4% | 44,836 | ⏭️ Skip |
| 2026-10-11 20:14 | V1009 | 3.6567% | 3.6619% ↑ | 2.7901% | 2.8450% ↑ | 13.6% | 44,698 | ⏭️ Skip |
| 2026-10-11 17:14 | V1011 | 5.1044% | 5.2203% ↑ | 1.7622% | 1.6608% ↓ | 32.4% | 44,800 | ✅ Deploy |
| 2026-10-11 17:14 | V1001 | 6.1670% | 6.1729% ↑ | 2.8112% | 2.8492% ↑ | 4.4% | 44,242 | ⏭️ Skip |
| 2026-10-11 17:14 | V1003 | 6.6024% | 6.6641% ↑ | 1.0105% | 1.0135% ↑ | 349.7% | 44,800 | ⏭️ Skip |
| 2026-10-11 17:14 | V1009 | 3.6545% | 3.6579% ↑ | 2.7769% | 2.8700% ↑ | 14.4% | 44,662 | ⏭️ Skip |
| 2026-10-11 14:14 | V1011 | 5.1035% | 5.1797% ↑ | 1.7692% | 1.7864% ↑ | 34.3% | 44,764 | ⏭️ Skip |
| 2026-10-11 14:14 | V1001 | 6.1681% | 6.1726% ↑ | 2.8155% | 2.8550% ↑ | 7.7% | 44,206 | ⏭️ Skip |
| 2026-10-11 14:13 | V1003 | 6.4841% | 6.5668% ↑ | 1.0106% | 1.0097% ↓ | 343.6% | 44,764 | ✅ Deploy |
| 2026-10-11 14:13 | V1009 | 3.6491% | 3.6510% ↑ | 2.7636% | 2.8680% ↑ | 4.0% | 44,626 | ⏭️ Skip |
| 2026-10-11 11:13 | V1011 | 5.1114% | 5.1094% ↓ | 1.7684% | 1.7817% ↑ | 28.4% | 44,728 | ✅ Deploy |
| 2026-10-11 11:13 | V1001 | 6.1663% | 6.1715% ↑ | 2.8201% | 2.8618% ↑ | 18.5% | 44,170 | ⏭️ Skip |
| 2026-10-11 11:13 | V1003 | 6.3631% | 6.4390% ↑ | 1.0103% | 1.0111% ↑ | 268.9% | 44,728 | ⏭️ Skip |
| 2026-10-11 11:13 | V1009 | 3.6356% | 3.6360% ↑ | 2.7489% | 2.8239% ↑ | 8.1% | 44,590 | ⏭️ Skip |
| 2026-10-11 08:13 | V1011 | 5.1101% | 5.2488% ↑ | 1.7690% | 1.7808% ↑ | 35.8% | 44,692 | ⏭️ Skip |
| 2026-10-11 08:13 | V1001 | 6.1628% | 6.1653% ↑ | 2.8277% | 2.8669% ↑ | 23.2% | 44,134 | ⏭️ Skip |
কীভাবে কাজ করে
📊
ডেটা সংগ্রহ
প্রতি ৩০ সেকেন্ডে নতুন metrics
📈
Drift শনাক্ত
নতুন vs পুরনো mean (threshold 10%)
🔄
Retrain
XGBoost নতুন ডেটা দিয়ে train
✅
যাচাই
পুরনো vs নতুন RMSE তুলনা
🚀
Deploy
উন্নত হলেই replace