🔧 MLflow & Evidently — A High-Level Overview

This repository uses MLflow and Evidently—two essential tools in modern MLOps—to solve real problems that appear after models are trained.

The focus here is what these tools are, why they exist, and when they are used, not how the code works.

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🌍 The Real Problem in Machine Learning

Training a model is only 10–20% of the real work.

The real challenges are:

• Multiple experiments with different models
• Losing track of parameters and results
• Not knowing which model is the best
• Models working well today but failing later
• Data changing silently in production

👉 MLflow and Evidently exist to solve these problems.

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📈 MLflow — Experiment Tracking & Model Management

🔹 What Is MLflow?

MLflow is a platform that helps you track, compare, and manage machine learning experiments.

It answers questions like:

• Which model performed best?
• What parameters were used?
• Can I reproduce this result?
• Which model should go to production?

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🔹 Why MLflow Is Needed

Without MLflow:

• Experiments are scattered across notebooks
• Results are lost or overwritten
• Reproducing models is difficult
• Team collaboration is painful

With MLflow:

• Every experiment is logged
• Every model is saved
• Everything is comparable and reproducible

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🔹 What MLflow Tracks

MLflow automatically or manually stores:

• Parameters
  (learning rate, C, kernel, solver, etc.)

• Metrics
  (accuracy, precision, loss, RMSE, etc.)

• Artifacts
  (trained models, plots, files)

• Runs & Experiments
  (organized experiment history)

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🔹 When MLflow Is Used

MLflow is used:

• During model development
• When testing multiple algorithms
• While tuning hyperparameters
• Before selecting a production model

📌 Think of MLflow as a version control system for ML experiments.

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🔍 Evidently — Model & Data Monitoring

🔹 What Is Evidently?

Evidently is a monitoring tool that checks whether your data and model behavior change over time.

It answers questions like:

• Is my production data different from training data?
• Has data drift occurred?
• Are predictions behaving abnormally?
• Do I need to retrain the model?

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🔹 Why Evidently Is Needed

In real life:

• User behavior changes
• Market conditions change
• Sensors degrade
• Data pipelines break

Even a perfect model will fail silently if data changes.

👉 Evidently helps you detect problems before users notice.

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🔹 What Evidently Monitors

Evidently focuses on:

• Data Drift
  Feature distributions changing over time

• Target Drift
  Change in actual outcomes

• Prediction Drift
  Model output behavior changing

• Data Quality Issues
  Missing values, invalid ranges, anomalies

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🔹 When Evidently Is Used

Evidently is used:

• After model deployment
• In production monitoring
• During model health checks
• To decide when retraining is needed

📌 Think of Evidently as a health monitor for deployed ML models.

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🧠 How MLflow & Evidently Work Together

Stage	Tool
Model development	MLflow
Experiment comparison	MLflow
Best model selection	MLflow
Deployment	(External tool)
Post-deployment monitoring	Evidently
Retraining decision	Evidently + MLflow

They solve different but connected problems.

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🔁 Simple Analogy

• MLflow → Lab notebook for scientists
  (records every experiment)

• Evidently → Medical checkup for models
  (checks model health over time)

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🚀 Why These Tools Matter for MLOps

Using MLflow and Evidently means:

• Models are traceable
• Results are reproducible
• Failures are detectable
• Systems are production-ready

They transform machine learning from:

“It works on my laptop”
to
“It works reliably in production.”

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🏁 Summary

• MLflow manages experiments, models, and results
• Evidently monitors data and model behavior
• Together they form the foundation of real-world MLOps

This repository demonstrates why these tools exist, not just how to use them.