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So far, you’ve learned:

Day 1 → What MLOps is and why it matters

Day 2 → ML lifecycle

Day 3 → Data engineering basics

Day 4 → Data drift and data quality

Today we answer another key question:

Why do companies carefully track every ML experiment, every model version, every parameter, and every result?

Because in real-world ML systems:

• dozens of experiments run daily

• hundreds of models are tested

• only a few go to production

• decisions must be traceable later

This process is called Experiment Tracking.

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1. First — What Is an ML Experiment?

An experiment is simply:

Trying different ways to train a model and checking which one works best.

Just like a chef experiments with:

• different spices

• different cooking times

• different ingredients

to improve taste.

In ML, we change:

• features

• algorithms

• parameters

• data samples

and check:

• accuracy

• fairness

• speed

• stability

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2. Why Tracking Experiments Is Necessary

Imagine a data science team in a bank testing 100 versions of a fraud model.

If they do not track:

• which data was used

• which code version

• which model version

• which parameters

• who trained it

• what accuracy it achieved

then later when someone asks:

Which model is in production?

Why was it approved?

Who built it?

Which data was used?

Can we reproduce it again?

No one can answer.

That is a huge business and legal risk.

So experiment tracking is mandatory in serious companies.

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3. What Exactly Do We Track?

Experiment tracking normally includes:

• dataset used

• data version

• code version

• model version

• hyperparameters

• accuracy metrics

• training environment

• hardware used

• experiment owner

• run time

• logs

This creates a complete history.

Just like a hospital keeps:

• patient reports

• prescriptions

• lab results

• treatment plans

so doctors can review and audit.

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4. Real-World Situations Where Tracking Saves the Day

Case 1 — Banking Audit

A regulator asks:

Why was this loan rejected by the model?

With experiment tracking, the bank can answer:

• version used

• data used

• logic applied

• probability score

• fairness evaluation

Without tracking → compliance failure and legal consequences.

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Case 2 — Bug Found in Model Code

Suppose an e-commerce recommendation model had a logic bug that reduced sales.

Tracking allows engineers to:

• identify the version with the bug

• rollback safely

• compare models

• understand impact

Without tracking → chaos.

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Case 3 — Reproducing Old Results

Suppose a hospital had an excellent model trained two years ago and wants to reproduce it.

Tracking allows:

• same data

• same code

• same parameters

• same environment

to recreate the model exactly.

This is called reproducibility.

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5. What Happens If You Don’t Track Experiments?

Common problems include:

• confusion about which model is best

• lost experiments

• duplicated work

• models deployed accidentally

• no audit history

• wrong models used in production

• team dependency on memory

• compliance failure

This is similar to software development without:

• version control

• CI/CD

• documentation

DevOps solved this for software.

MLOps solves this for ML models.

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6. Tools Used for Experiment Tracking

Popular tools include:

• MLflow

• Weights & Biases

• Neptune

• Comet

• Kubeflow Tracking

Core features usually include:

• logging runs

• comparing experiments

• tracking metrics

• storing artifacts

• registering models

MLOps teams standardize this across the organization.

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7. Introducing the Model Registry

A Model Registry is like GitHub, but for ML models.

It keeps track of:

• model versions

• lifecycle stage (staging, production, archived)

• approvals

• deployment history

This ensures no model goes to production without control.

Banks, healthcare, fintech — all rely on this discipline.

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8. Experiment Tracking vs Model Registry — Simple Difference

So first we experiment,

then we promote the best model into registry,

then we deploy it.

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9. Real-World Analogy

Think of pharmaceutical drug development.

Step 1 — many formulas tested in lab

Step 2 — every trial recorded

Step 3 — best one approved

Step 4 — approved formula stored securely

Step 5 — only approved formula used in production

ML works similarly.

Tracking ensures safety and trust.

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10. What Metrics Do We Track?

Depends on problem.

Fraud Detection

• false positives

• false negatives

• precision

• recall

Recommendation Systems

• click-through rate

• conversion rate

Healthcare

• sensitivity

• specificity

• risk accuracy

Food Delivery

• ETA accuracy

• route efficiency

These metrics tell us which model works best.

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11. Experiment Tracking in the Context of DevOps

In DevOps, we track:

• commits

• builds

• releases

• logs

In MLOps, we track:

• experiments

• datasets

• models

• pipelines

• metrics

So MLOps extends DevOps discipline into ML systems.

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12. A Simple Story to Remember

A food delivery company is testing a new ETA prediction model.

They run:

• 50 experiments

• using 3 months of data

• changing 10 parameters

Without tracking:

No one remembers which model was best.

With tracking:

They select the winning model confidently

deploy it

monitor it

retrain when needed

And everything is traceable later.

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13. Quick Recap of Day 5

Today you learned:

• what ML experiments are

• why tracking is necessary

• what needs to be logged

• risks of not tracking

• model registry concept

• real-world industry examples

• audit and compliance importance

• MLOps + DevOps connection

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