The 5 Stages of a Successful ML Project (And Why Most Fail Before They Start)

Learn the 5 key stages of a successful ML project and how to build one that stands out for job applications, portfolios, or production. 🚀

Machine Learning projects often fail—not because the model isn’t good, but because the process is broken. If you’ve ever started an ML project only to abandon it halfway, you’re not alone.

The good news? ML projects follow predictable stages. Master these, and you’ll massively improve your chances of building something that actually works—whether for a job application, a side project, or production use.

And since it’s almost Valentine’s Day, let’s lean into the theme—because, like relationships, ML projects go through key phases before they either flourish or fail.

Courtship: Falling in Love with the Idea

Every ML project starts with excitement. You’ve found the one—an idea that makes your heart race. You start fantasizing about how perfect it will be, how impressive it will look on your resume, and how everyone will love it.

But just like in dating, jumping in too fast is a mistake. Before you commit, ask yourself:

✅ Is this a problem worth solving?
✅ Can you get data easily?
✅ Would people actually use it?

Many ML projects fail right here because they start as solutions looking for problems. Instead of chasing hype, find a problem you personally care about—this will keep you invested when things get tough.

Scoping: Defining Success Before You Commit

Are you setting yourself up for a lasting commitment or a heartbreak? The biggest killer of ML projects? Trying to do too much, too soon.

💡 Think like a healthy relationship: What’s the MVP (minimum viable project)? What’s the smallest version that is still useful?

I will write more about how to scope in the near-future, this is a critical step. For now, don’t just jump in, have a plan!

Data: Getting Your House in Order Before Moving In

If scoping is like defining relationship expectations, data is where you check the financials. Just like you wouldn’t move in with someone without discussing rent and expenses, you shouldn’t dive into modeling without securing your data.

Data is to ML what trust is to relationships—without it, you have nothing. But here’s the catch: Most ML engineers spend too little time on data and too much on models.

Before even thinking about training:

✅ Where will the data come from? (APIs, scraping, public datasets, synthetic data?)
✅ How much do you need? (Is it enough to generalize?)
✅ Is labeling required? (If so, how will you do it efficiently?)

Many projects get stuck here because people underestimate data cleaning and preprocessing. Treat this phase seriously—it will make or break your project.

Modeling: The Honeymoon Phase (But Keep It Simple)

Ah, the rush of training your first model—the ML equivalent of the honeymoon phase. Everything seems magical. But just like in love, things get complicated fast if you overcomplicate them.

🔹 Start with a baseline—don’t jump straight into deep learning if logistic regression can do the job.
🔹 Optimize for reliability, not novelty—reliability matters more than using the latest transformer model.
🔹 Track your experiments—keep logs so you can compare different versions.

Think of modeling like cooking for a date: Start simple, taste frequently, and iterate. Avoid “chef syndrome” where you try to add every ingredient at once.

Deployment: The ‘Meet the Parents’ Stage

Most ML projects never make it to this stage. But if you get here, congrats—you’ve built something real. Now comes the true test: making it work in the real world.

Before deployment, ask:

✅ How will users interact with it? (Web app? API? CLI tool?)
✅ How will you handle updates? (ML models drift over time—plan for retraining.)
✅ How do you monitor it? (Log predictions, track errors, collect feedback.)

Just like meeting the parents, deployment reveals whether your project can handle real-world scrutiny. Not every project needs a fancy UI—an email digest or CLI tool can be just as impactful. Focus on getting real users over building an over-engineered interface.

Final Thought: Why You Should Document Everything

If you’re building an ML project for a job application, a portfolio, or even for fun, document your journey.

📖 Write down what was hard. Hiring managers love problem-solvers.
📊 Collect usage data. “50,000 daily users” is way more impressive than “98% accuracy on a test set.”
📝 Turn your journey into a blog post. Great for learning, branding, and standing out.

Master these five stages, and you’ll not only build better ML projects—you’ll also massively increase your chances of getting hired, making an impact, or even launching a real product.

💖 Just like in relationships, the key to a great ML project is commitment, adaptability, and knowing when to keep it simple.

🚀 Want more ML career insights? Subscribe and check out MLEPath.com for expert guidance!

Happy Valentine’s Day!