Tracking machine learning experiments isn’t a luxury—it’s essential. As a Junior AI Engineer working on multiple models and pipelines, I’ve learned how critical experiment tracking becomes once your project moves beyond a Jupyter notebook.
In this post, I’ll walk you through:
- 🔄 Why experiment tracking matters
- 🧰 The difference between MLflow and W&B
- ⚙️ How I use them in real projects
- ✅ When to use which tool
🚨 The Problem
You trained a model last week. It worked. But now…
- What features did you use?
- What hyperparameters gave the best accuracy?
- Where’s the version of the dataset you used?
Without tracking, you’re relying on memory (bad idea) or scattered notes (worse idea).
🛠️ MLflow vs. Weights & Biases
| Feature | MLflow | Weights & Biases (W&B) |
|---|---|---|
| Setup | Simple, local-first | SaaS + Local support |
| UI | Minimal, self-hosted | Rich, interactive dashboard |
| Logging | Metrics, params, artifacts | Metrics, params, images, more |
| Integration | Great with Python + REST API | Strong for deep learning |
| Hosting | Self or Databricks | Free cloud tier available |
| Use Case | Classical ML, corporate use | Deep learning, team projects |
⚙️ My Setup (Real-World Use)
MLflow
I use it for:
- Sklearn pipelines
- Traditional ML models (XGBoost, Random Forest)
- Tracking metrics & saving artifacts
- Auto-logging with
mlflow.sklearn
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
with mlflow.start_run():
model = RandomForestClassifier()
model.fit(X_train, y_train)
mlflow.sklearn.log_model(model, "model")
mlflow.log_metric("accuracy", accuracy_score(y_test, model.predict(X_test)))
## 🧪 Weights & Biases
**Used for:**
- Deep learning (Keras / PyTorch)
- Logging training curves, images, system metrics
- Comparing dozens of runs interactively
python
import wandb
from wandb.keras import WandbCallback
wandb.init(project=“cnn-project“)
model.fit(X_train, y_train, epochs=10, callbacks=[WandbCallback()])
🧠 Lessons Learned
Log everything early. You’ll thank yourself later.
Pick the right tool for the job: MLflow for structured ML, W&B for dynamic DL.
Use tags and versioning so your team (or future self) can make sense of experiments.
📌 Final Thoughts
Experiment tracking is like version control for your brain.
If you’re working on even slightly complex projects, start logging today—before you’re 20 experiments deep in chaos.
Have you used MLflow or W&B?
Or do you rely on spreadsheets and screenshots (no judgment 😅)?
I’d love to hear your workflow in the comments below!