MLOps Fundamentals MLOps Tools and Technologies

Overview

MLOps relies on a combination of tools to manage the machine learning lifecycle, including experiment tracking, automation, and deployment. This lesson introduces MLflow, a widely used MLOps tool for experiment tracking, model management, and reproducibility, through a hands-on walkthrough.

Learning Objectives

By the end of this microlesson, you will:

Understand the role of different MLOps tools.
Use MLflow to track and compare ML experiments.
Log metrics, parameters, and models in MLflow.

1. Common MLOps Tools

MLOps solutions fall into different categories:

Category	Tools
Experiment Tracking	MLflow, Weights & Biases, Neptune.ai
Model Versioning	MLflow Model Registry, DVC, Pachyderm
CI/CD for ML	GitHub Actions, Jenkins, Kubeflow Pipelines
Model Deployment	MLflow, TensorFlow Serving, Seldon Core, Kubernetes
Monitoring & Drift Detection	EvidentlyAI, WhyLabs, Prometheus, Grafana

2. Hands-On: Tracking Experiments with MLflow

Step 1: Setup MLflow

Install MLflow if you haven’t already:
```
pip install mlflow
```
Start the MLflow tracking server:
```
mlflow ui
```
Open http://localhost:5000 to access the MLflow dashboard.

Step 2: Log an ML Experiment

Modify your training script to log parameters, metrics, and models.

Before (No Experiment Tracking):

model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")

After (Using MLflow):

import mlflow
import mlflow.sklearn

mlflow.start_run():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    
    mlflow.log_param("model_type", "RandomForest")
    mlflow.log_metric("accuracy", acc)
    mlflow.sklearn.log_model(model, "model")

Run the script and check MLflow UI to see logged parameters and metrics.

3. Key Takeaways

✅ MLOps tools streamline the ML lifecycle.
✅ MLflow enables experiment tracking, model versioning, and deployment.
✅ Logging with MLflow makes ML workflows reproducible and scalable.