Experiment Tracking Systems in ML Version Control

Tracking experiments is a crucial part of the machine learning (ML) lifecycle. As models evolve, keeping track of hyperparameters, datasets, and results helps ensure reproducibility and optimizes performance. Experiment tracking systems like MLflow, Weights & Biases, and DVC enable efficient logging and comparison of different model runs.

Why Use Experiment Tracking?

• Reproducibility: Maintain a history of model runs and hyperparameters.

• Comparison: Evaluate multiple experiments to select the best model.

• Collaboration: Share experiment results with teammates.

• Automation: Integrate tracking into ML pipelines.

Setting Up MLflow for Experiment Tracking

Step 1: Install MLflow

pip install mlflow

Step 2: Initialize an MLflow Experiment

import mlflow
mlflow.set_experiment("my_experiment")

Step 3: Log Parameters and Metrics

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.92)

Step 4: Save Models

from sklearn.ensemble import RandomForestClassifier
import mlflow.sklearn

model = RandomForestClassifier()
mlflow.sklearn.log_model(model, "random_forest_model")

Step 5: View Logged Data

Run the following command to start the MLflow UI:

mlflow ui

This launches a dashboard where all experiments can be visualized.

Using Weights & Biases (W&B) for Experiment Tracking

Step 1: Install W&B

pip install wandb
wandb login

Step 2: Log an Experiment

import wandb
wandb.init(project="my_project")

wandb.log({"loss": 0.23, "accuracy": 0.91})

Best Practices for Experiment Tracking

1. Use Unique Experiment Names: Avoid confusion between different runs.

2. Automate Logging: Integrate tracking into training scripts.

3. Store Metadata: Log model architecture, datasets, and parameters.

4. Utilize Visualization Tools: Compare model performance effectively.

Conclusion

Experiment tracking systems like MLflow and W&B streamline ML development, ensuring that models are optimized efficiently. Implementing experiment tracking enhances reproducibility, collaboration, and model performance.