Junior MLOps Engineer - Day 2 Training: ML Lifecycle Deep Dive

 

Goal: Understand how a machine learning system moves from raw data to a live, monitored production model—and where things can break.

Why the ML Lifecycle Matters

Many beginners think machine learning ends after training a model.
In reality, training is just the middle.

Most real-world ML failures happen after deployment, not during modeling.

MLOps exists to manage the full lifecycle.

1. Data

What happens:

• Collect raw data (logs, images, text, transactions, etc.)

• Clean, label, and validate data

• Split into train / validation / test sets

Common failure points:

• Missing or incorrect labels

• Biased or unrepresentative data

• Data leakage (future data in training)

2. Training

What happens:

• Select algorithms

• Train models on historical data

• Tune hyperparameters

• Evaluate performance (accuracy, precision, recall, etc.)

Outputs:

• Model artifact (file)

• Metrics

• Training logs

Common failure points:

• Overfitting to training data

• Training on outdated data

• Metrics that don’t reflect real-world usage

3. Model (Artifact Management)

What happens:

• Save trained models

• Version models

• Track metadata (data version, parameters, metrics)

Why this matters:
Without versioning, you can’t answer:

“Which model caused this bad prediction?”

Common failure points:

• No version control

• No experiment tracking

• Can’t reproduce results

4. Deployment

What happens:

• Expose the model via:

  • API (real-time predictions)

  • Batch jobs (scheduled predictions)

• Integrate into applications or workflows

Deployment types:

• Canary

• Blue/Green

• Shadow deployments

Common failure points:

• Environment mismatch (works in training, fails in prod)

• Latency issues

• Scaling failures

5. Monitoring

What happens:

• Track:

  • Prediction accuracy

  • Input data changes

  • Model performance over time

• Detect drift and anomalies

Types of monitoring:

• Data drift (input changes)

• Concept drift (pattern changes)

• Infrastructure health

Common failure points:

• No monitoring at all

• Monitoring only uptime, not accuracy

• Ignoring early warning signals

Feedback Loops & Retraining

Why Feedback Loops Are Critical

The real world changes:

• User behavior shifts

• Fraud tactics evolve

• Language changes

• Markets fluctuate

Feedback loop process:

1. Monitor performance

2. Detect degradation

3. Collect new data

4. Retrain model

5. Redeploy improved version

This loop is what keeps models alive and useful.

How MLOps Fits In

MLOps ensures:

• Every stage is repeatable

• Failures are detectable

• Updates are safe

• Models are maintainable

Without MLOps: models slowly die.
With MLOps: models evolve