Introduction

When I first heard the term MLOps, I thought it was just “DevOps but for ML.”
Simple enough, right? Deploy some models, automate a few scripts, done.

But the deeper I went, the more I realised:
Machine Learning moves differently. Models drift. Data changes. Accuracy drops. Pipelines break. And unlike a normal application, ML systems need constant monitoring and retraining.

That’s where MLOps comes in.
It brings order to the chaos of machine learning workflows.

Let’s walk through MLOps in a beginner-friendly, practical way.

My Earlier Understanding of MLOps

I assumed MLOps was mostly about:

• Deploying ML models
• Training automation
• Using tools like MLflow or Kubeflow

Pretty straightforward.

But every real-world ML project quickly breaks that illusion:

• Datasets keep changing
• Model accuracy slowly decreases
• Experiments multiply fast
• Keeping track of versions becomes messy
• Improving one part breaks another

Basically: ML models don’t stay stable. They age like fruit, not like software.

And that’s exactly why MLOps exists.

My New Understanding of MLOps

Now I understand that MLOps = DevOps + Machine Learning Lifecycle.

It is a set of practices that help you:

• Build
• Train
• Deploy
• Monitor
• Re-train
• Scale

machine learning models reliably.

Why do we need MLOps?

Because ML systems are alive.
The data changes → the model changes → performance changes → business impact changes.

MLOps ensures:

• Models stay accurate in production
• Retraining happens automatically
• Data pipelines remain stable
• Experiments are traceable
• Deployments are repeatable
• Teams collaborate smoothly

Without MLOps, ML systems collapse fast.

Core Stages of the MLOps Lifecycle

Here’s the flow most MLOps setups follow:

1. Data Collection

Collecting raw data from APIs, streams, databases, or logs.

2. Data Versioning

Using tools like DVC, Delta Lake, or LakeFS to track dataset versions.

3. Data Preprocessing

Cleaning, labeling, transforming, and featurizing data.

4. Experimentation

Running multiple model experiments using:

• MLflow
• Weights & Biases
• TensorBoard

Everything (metrics, parameters, accuracy) gets tracked.

5. Model Training

Training models at scale using GPUs or distributed clusters.

6. Model Packaging

Converting models into deployable formats:

• SavedModel
• ONNX
• Pickle
• TorchScript

7. Model Deployment

Deploying models on:

• REST APIs
• Batch pipelines
• Edge devices
• Kubernetes

Tools: BentoML, FastAPI, TorchServe, Vertex AI.

8. Monitoring

Tracking:

• Model accuracy
• Latency
• Data drift
• Prediction drift
• Concept drift

9. Continuous Retraining

Automated pipelines retrain models when performance drops.

This cycle keeps repeating.

Key Concepts You Must Know

Model Versioning

Because models evolve with new data.

Feature Stores

Centralized place to store and reuse features across models.
Examples: Feast, Hopsworks.

Data Drift & Concept Drift

Drift is the biggest reason models fail in production.

Model Registry

A catalog of models, versions, and metadata.

Orchestration Pipelines

Automated workflows using:

• Kubeflow Pipelines
• Airflow
• Flyte
• Metaflow

CI/CD for ML (CI/CD/CT)

Besides code, ML systems need:

• Continuous Training (CT)
• Continuous Monitoring (CM)

Example MLOps Stack

A modern MLOps workflow might include:

• GitHub / GitLab – Code versioning
• DVC / LakeFS – Data versioning
• MLflow / W&B – Experiment tracking
• FastAPI / BentoML – Model serving
• Kubernetes – Scaling models
• Prometheus + Grafana – Monitoring
• Airflow / Kubeflow – Pipelines
• S3 / MinIO – Artifact storage

Together, they create a complete ML ecosystem.

Real-World Use Cases

MLOps is behind most AI-powered systems you interact with daily:

• Netflix recommendation engine
• Fraud detection models
• Autonomous vehicles
• Chatbots & NLP systems
• E-commerce ranking algorithms
• Healthcare prediction models

Without MLOps, these systems would break quickly.

Resources That Helped Me

• Google MLOps Guide
  Best conceptual explanation of ML production systems.

• Made With ML
  Easy tutorials for beginners.

• MLflow Documentation
  Perfect for learning tracking, registry, and deployment.

• YouTube: Goku Mohandas
  Clear breakdowns of modern MLOps patterns.

Conclusion

MLOps is no longer optional—it’s essential.
As AI grows, so does the need for stable, reliable machine learning pipelines.

Understanding MLOps helps you:

• Build scalable ML systems
• Automate complex workflows
• Track experiments cleanly
• Deploy models confidently
• Monitor them reliably

This blog is just the beginning.
Soon, I’ll dive deeper into:

• Model versioning explained
• Building pipelines with Kubeflow
• Deploying ML models using FastAPI
• Understanding data drift & retraining
• End-to-end MLOps project for beginners

MLOps isn’t just a workflow—it’s the backbone of real-world AI.