Looking for some advice from people who’ve run into this before.

I retrained a customer churn model using the latest six months of data and saw a noticeable jump in training performance, but validation metrics dropped significantly. The model now performs extremely well on the training set and much worse on unseen data.

I’ve already checked for data leakage and haven’t found anything obvious. Feature engineering hasn’t changed much, although customer behavior has shifted slightly over the last quarter.

Current setup:
• Binary churn prediction
• Gradient boosting model
• Monthly retraining cycle
• Class imbalance handled through weighting

Has anyone experienced something similar after retraining? What would you investigate first—data drift, feature importance changes, retraining window, regularization, or something else?

Would appreciate any suggestions or lessons learned.

I’ve been exploring how organizations are structuring production-ready AI workflows beyond just model experimentation, particularly around orchestration, retrieval pipelines, memory handling, monitoring, and multi-agent coordination.

There are now so many combinations being used across:
• LLM frameworks
• vector databases
• orchestration layers
• observability tools
• retrieval systems
• agent frameworks
• cloud infrastructure

The challenge is that many stacks work well in prototypes, but reliability, scalability, governance, and operational complexity become very different conversations once systems move into real enterprise environments.

Curious to hear from teams already building or deploying AI agents in production:
What stack combinations are working well for you, and what trade-offs have you encountered so far?

I’m working on a data science project with time-ordered data, and I’m seeing a significant drop in model performance once I move from training to validation. I’m sharing a simplified version of the problem and code below.

The dataset represents events over time, and the target is binary. I initially used a random train-test split, but later switched to a time-based split to better reflect real-world usage. After this change, performance dropped sharply, and I’m trying to understand whether this is expected or if I’m doing something wrong.

Here’s a simplified version of the code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

# sample data
df = pd.read_csv("data.csv")
df = df.sort_values("event_time")

X = df.drop(columns=["target"])
y = df["target"]

# time-based split
split_index = int(len(df) * 0.8)
X_train, X_test = X.iloc[:split_index], X.iloc[split_index:]
y_train, y_test = y.iloc[:split_index], y.iloc[split_index:]

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

preds = model.predict_proba(X_test)[:, 1]
print("AUC:", roc_auc_score(y_test, preds))

With a random split, the AUC was around 0.82.
With the time-based split, it drops to around 0.61.

I’m trying to understand:

• Is this performance gap a common sign of data leakage in the original setup?

• Are tree-based models like Random Forests particularly sensitive to temporal shifts?

• What are good practices to diagnose whether this is concept drift, feature leakage, or simply a harder prediction problem?

• Would you approach validation differently for time-dependent data like this?

Looking for general guidance, validation strategies, or patterns others have seen in similar scenarios.