Why 87% of ML models never make it to production (and how to be in the 13%)

The Hidden Challenge of Deploying ML Models (And How We'll Solve It Together)

Hey there,

Let me tell you a story that might sound familiar...

You've just trained an amazing ML model. It's accurate, it's fast, and it solves a real problem. You're excited to share it with the world. But then comes the dreaded question:

"How do I actually deploy this thing?"

If you’ve been stuck at that moment—staring at your Jupyter notebook, unsure what’s next—you’re not alone.

📉 According to Gartner, 87% of ML models never make it to production.

Not because they’re bad models, but because turning them into reliable services is… messy!

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The Problem Nobody Talks About

Here's what typically happens:

• Monday: "Let me just put this model behind a Python FastAPI" 😊

• Tuesday: "Oh, I need Docker for consistency" 🤔

• Wednesday: "Wait, how do I update the model without downtime?" 😰

• Thursday: "The new model is worse! How do I rollback?" 😱

• Friday: "Everything is on fire 🔥"

Sound familiar?

The truth is, deploying ML models in production isn't just about making predictions available via an API.

It’s about building a system that:

• Updates models without downtime

• Rolls back when things go wrong

• Detects performance issues

• Handle real production traffic

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What We're Building Together

Over the next few posts, I’ll guide you step-by-step to build your own self-hosted MLOps pipeline—no AWS, no expensive services.

Starting Point: Model on your laptop

⬇

End Goal: A full ML deployment system with:

• Zero-downtime model updates

• Instant rollback support

• Real-time monitoring

• Self-hosted & scalable

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The Journey Ahead

Here's what we'll cover in upcoming posts:

Understand the Architecture

→ What real ML systems look like

Containerize + Version Models

→ So you never ship “model_final_v2_really_final.pkl” again

Build the Deployment Pipeline

→ Blue-green deploys, traffic routing, rollback safety nets

Add Monitoring & Automation

→ Track drift, trigger retraining, and sleep well

What Makes This Series Different

We’re not building toy projects.

We’ll use real tools (Docker, Git...) on your own server to solve real deployment problems.

You don’t need to be a DevOps guru. Just follow along, and you'll:

✅ Build a portfolio-worthy MLOps project
✅ Learn skills that companies pay for
✅ Finally ship your ML projects to real users

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Before our next post, think about this:

What's the scariest part of deploying your ML model to production?

• Is it handling traffic spikes?

• Updating models without downtime?

• Knowing when something goes wrong?

• Rolling back failed deployments?

Reply and let me know.

I'll address the most common concerns in our upcoming issues.

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Here's What Happens Next

1. Next Week: We’ll break down what a real production-ready ML system looks like — piece by piece.

2. Following Weeks: Step-by-step tutorials with actual code you can run

3. Final Outcome: You'll have a complete MLOps pipeline running on your own infrastructure

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A Quick Preview — Spoiler!

Here's a taste of what you'll be able to do:

# Deploy a new model with zero downtime

./deploy.sh my_awesome_model_v2

# Watch the magic happen

Validating model... ✓

Building container... ✓

Starting canary deployment (5% traffic)... ✓

No errors detected, expanding to 25%... ✓

Expanding to 100%... ✓

Draining old connections... ✓

Deployment complete! Zero requests dropped.

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Join Me on This Journey

Over the coming weeks, we'll transform from "It works on my machine" to "It works in production at 3 AM while I'm sleeping."

Fun fact: The clock shows 5 because even AI doesn't want to acknowledge that 3 AM debugging sessions exist. It's in denial, just like we all are.

Next week, we'll start with the big picture - the architecture that makes all of this possible.

Until then, keep training those models.

Soon, they'll all make it to production.

P.S. Have a friend struggling with ML deployment? Forward this to them. We're all in this together.

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Your First Assignment!

Let's get our hands dirty!

In the next 10-15 minutes, you'll build a working sentiment analysis API from scratch.

No cloud accounts needed, no complex setup - just Python and some command line magic.

By the end, you'll have a real ML model serving predictions through a REST API.

Here's how we'll do it:

Step 1: Clone the GitHub Repo:

git clone https://github.com/hassancs91/MLOps-For-Beginners.git

This will be our repo for this series.

Step 2: Install Requirements

Make sure you're using a clean virtual environment:

pip install -r requirements.txt

Step 3: Train the Sentiment Model

cd 00_model_baseline

python train.py

This will:

• Train a Logistic Regression model on airline tweets

• Preprocess the text with TF-IDF

• Save model artifacts to models

Step 4: Run Local Inference (CLI)

python inference.py "I love flying with this airline!"

You will get something like:

Or run it interactively:

python inference.py

Step 5: Start the FastAPI Server

uvicorn app:app --reload --host 0.0.0.0 --port 8000

Then open your browser and test at:

http://localhost:8000/docs

You’ll find endpoints for:

• POST /predict — Analyze a tweet

• GET /health — Health check

• GET /model-info — Model metadata

You now have a local ML pipeline running end-to-end — ready to be containerized, monitored, and deployed.

Common Issues you may face:

- Windows users: Use `python` instead of `python3`

- Port 8000 in use: Change to `--port 8001`

- Module not found: Make sure you activated your virtual environment!

- Still stuck? reply - I'm here to help!

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You have a working ML pipeline.

But here's the million-dollar question:

How do you get this from your laptop to serving millions of users without it falling apart?

Starting Next Week, we'll transform this simple API into a bulletproof production system.

One that updates without downtime, monitors itself, and sleeps soundly through traffic spikes!