The Machine Learning Lifecycle: All Stages Explained

What Is the Machine Learning Lifecycle?

Ask five people how many stages the ML lifecycle has and you'll get five different numbers. Welcome to the club.

Some say five, some say eight, and TutorialsPoint-style pages tend to land somewhere in between depending on the day. Nobody's lying here, they're just drawing the boundary lines differently. Almost every version traces back to CRISP-DM, a data mining process framework from 1996 that boiled the whole thing down to: understand the business problem, understand the data, prepare it, model it, evaluate it, deploy it, and go back and do it again. Everything published since is basically a remix of that.

If you want the family tree of how these frameworks evolved, this rundown of CRISP-DM's lineage is worth a look.

For this article, we're going with seven stages, because that's the count that actually maps to what a junior ML engineer will touch on their first real project:

1. Problem Definition & Success Metrics

2. Data Collection

3. Data Preparation & EDA

4. Feature Engineering & Selection

5. Model Selection & Training

6. Model Evaluation

7. Deployment, Monitoring & Retraining

That's the skeleton. Most articles stop there and call it a day. We're not going to do that.

Instead of talking about “the model” in the abstract, which is how most of these guides read, we'll carry one project through all seven stages. It also helps to know there are different flavours of learning happening underneath all this; if that's unfamiliar, this primer on types of machine learning is a decent five-minute detour before you come back.

The project: a telecom company trying to figure out which customers are about to churn, meaning cancel their plan and wander off to a competitor offering a marginally cheaper data pack. It's not glamorous. Nobody builds a churn model for fun. But it's a real business problem that shows up in interviews, take-home assignments, and actual jobs constantly, which makes it the perfect thread to follow.

Stage 1: Problem Definition & Success Metrics

This is the stage everyone skips and then pays for later.

“Reduce churn” is not an ML problem. It's a wish. Somebody on the ML team has to turn it into something a model can actually be trained and scored against: predict, for each active customer, the probability that they cancel their subscription in the next 30 days. Now it's a binary classification problem with a time window, which is something you can build.

This is also where the team decides what “good” looks like before a single line of data-loading code gets written. For the churn project, that meant agreeing the business cares more about catching likely churners (recall) than about being paranoid and flagging loyal customers by mistake (precision), because a retention call to a happy customer costs a lot less than losing a paying one silently.

The common failure mode

Solving the wrong problem, really, really well. A model that predicts churn with 95% accuracy sounds impressive until someone points out that only 4% of customers churn in any given month, so a model that just predicts “no one churns” would already be right 96% of the time and completely useless. This is the classic imbalanced-classes trap, and it starts right here, not at the modeling stage where people usually blame it.

You're done with this stage when

You have a single-sentence problem statement, a target variable everyone agrees on, and a metric that reflects what the business actually cares about, not just what's easy to compute.

Stage 3: Data Preparation & EDA

Here's the part nobody puts on their LinkedIn banner: this stage eats more time than anything else in the project. Practitioner surveys have said it for years, data scientists spend the bulk of their time cleaning and wrangling data rather than building models, and the churn project is no exception. Expect missing values in the usage logs (sensors and logging pipelines fail quietly), duplicate customer records after a CRM migration nobody documented properly, and outliers like a customer with negative account tenure because someone fat-fingered a date field.

EDA is where patterns start to surface. Plotting tenure against churn rate for the telecom data typically shows a sharp cliff in the first three months, new customers bail fast if onboarding goes badly, then churn risk flattens out. That single chart usually ends up shaping which features get engineered next. For a structured approach to this kind of cleanup work, this guide to data cleaning in machine learning and this overview of data preprocessing are both good references.

The time-allocation reality check

If you're new to this and expecting a 50/50 split between data work and modeling, adjust your expectations now. A realistic breakdown on a project like this looks closer to 60 to 70 percent on data collection, cleaning, and EDA, maybe 15 percent on feature engineering, and the rest split between training, evaluation, and deployment. Modeling is the fun part. It's also the smallest part.

You're done with this stage when

Missing values and outliers have a documented handling strategy (not just deleted and forgotten), and your EDA has produced at least a couple of hypotheses about what drives churn.

Want hands-on practice with this stage specifically? Scaler's free EDA & Data Visualisation course walks through exactly this kind of messy, real-world dataset.

Link: Scaler's free EDA & Data Visualisation course

Stage 4: Feature Engineering & Selection

Raw columns rarely do the heavy lifting. Nobody's tenure_in_days field is going to save your model on its own. The real signal usually comes from features someone has to build: tenure buckets (0 to 3 months, 3 to 12, 12 plus), a usage-trend feature comparing this month's data consumption against the trailing three-month average, a support-ticket-count-in-last-30-days feature, and a payment-delay flag.

Selection matters just as much as creation. Throwing 200 half-useful features at a model doesn't make it smarter, it usually makes it noisier and slower to train, and harder to explain to a stakeholder who wants to know why a specific customer got flagged. For the churn project, feature selection trimmed an initial set of around 60 candidate features down to roughly 18 that actually moved the needle.

This stage deserves its own deep dive, which is exactly what this piece on feature engineering in machine learning covers in more detail.

You're done with this stage when

Every feature in your final set has a defensible reason for being there, and you can explain it to someone outside the ML team without losing them halfway through.

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Stage 5: Model Selection & Training

Start with a baseline. Always. A logistic regression or a simple decision tree, trained in an afternoon, tells you what “bad” actually looks like for this problem before you go chasing a fancy gradient-boosted ensemble that takes three days to tune. For churn prediction specifically, the shortlist usually looks like logistic regression as the baseline, random forest as a solid middle ground, and XGBoost or LightGBM if the team has the bandwidth to tune it properly.

Hyperparameter tuning happens here too, and so does cross-validation, which matters more than most beginners realize. A single train-test split can get lucky (or unlucky), and k-fold cross-validation is the standard way to make sure your reported performance isn't just an accident of which rows landed in the test set.

The common failure mode

Skipping the baseline and going straight for the complicated model. It feels productive. It's usually a waste of a week, because without a baseline you have no idea whether the fancy model is actually earning its complexity or just matching what a five-line logistic regression would've given you anyway.

You're done with this stage when

You've beaten your baseline by a margin that actually matters to the business, not just a margin that looks nice on a slide.

Stage 6: Model Evaluation

This is where accuracy quietly betrays you again. Because churn is rare (again, roughly 4% of customers in a given month), accuracy is close to meaningless on its own. Recall and F1-score matter more, since missing an actual churner (a false negative) is expensive, while flagging a loyal customer for a retention call (a false positive) is annoying but cheap by comparison.

Evaluation isn't purely a numbers exercise either. Business validation matters just as much: does the model's top-100 highest-risk customer list actually make sense to the retention team, or does it look like a random sample with extra steps? A model that scores well on paper but produces a customer list the retention team doesn't trust is a model nobody will actually use.

Deep dive on choosing and interpreting the right metrics for imbalanced problems like this coming soon on Scaler's evaluation metrics page.

You're done with this stage when

Held-out test performance and the domain team's gut check both point in the same direction. If they disagree, that's not a green light, that's a conversation you need to have before deployment, not after.

Stage 7: Deployment, Monitoring & Retraining

This is the stage that separates “I finished the course” from “I've shipped something.” It's also the stage most courses conveniently skip, which is a shame because it's arguably where most of the real-world pain lives.

First decision: batch or real-time. For churn prediction, batch scoring usually wins, since you're generating a risk score for every customer once a week or once a day, not reacting to a live event in real time. That's a much simpler serving pattern than standing up a low-latency API, and there's no reason to over-engineer it just because real-time sounds cooler in a design doc.

Then comes the part nobody warns you about enough: data drift. Six months after launch, the telecom rolls out a new pricing plan, and suddenly the usage patterns the model was trained on don't look like the usage patterns customers actually have anymore. The model doesn't throw an error. It just gets quietly worse, which is somehow scarier than an outright crash because nobody notices until someone asks why churn is climbing despite the model supposedly catching it.

Monitoring for this kind of drift, and setting up retraining triggers before performance visibly degrades, is the whole point of MLOps. If you're going deeper into how teams actually automate this loop, this walkthrough of the MLOps pipeline is the natural next stop, and AWS's Machine Learning Lens documentation is a solid, if dense, reference for how a cloud-scale team thinks about the same problem.

And here's the part that trips up a lot of people coming from a course-based background: the lifecycle is a loop, not a line. Deployment isn't the finish line, it's the point where the model starts generating the next round of training data, which eventually feeds back into stage 2. The arrow at the end points back to the start, not to a trophy.

You're done with this stage when

You never really are. You have monitoring dashboards, defined drift thresholds, and a retraining schedule, but “done” isn't really a word that applies to a deployed model. It's more of a “maintained until further notice” situation.

Roles, Tools & Common Pitfalls Across the Lifecycle

Different stages tend to pull in different people, and the tooling shifts with them. Here's a rough map, based on how a mid-sized team would typically split this work:

If this table is the moment it clicks that the lifecycle is bigger than “train a model in a notebook,” that's the whole point of this article. Building the muscle to work across all seven stages, not just the fun middle one, is what separates people who finish courses from people who actually ship things.