The ML Lifecycle in One Picture
Data, train, evaluate, deploy, monitor, retrain as a loop.
The ML Lifecycle in One Picture is a free MLOps Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
One Big Loop
The whole ML lifecycle fits in one picture: data, train, evaluate, deploy, monitor, retrain. It is not a line, it is a circle that keeps turning. 🔄
It Starts with Data
Every model begins with data: collecting it, cleaning it, and labeling it. Garbage in here means garbage everywhere downstream.
The Training Step
Next you train: the model learns patterns from your prepared data. This is the step most people picture when they think of ML.
model.fit(X_train, y_train)Evaluate Honestly
Before you trust a model you evaluate it on data it never saw. A held-out test set tells you if it really learned or just memorized.
Deploy to Serve
To deploy means putting the model where it can answer real requests, usually behind an API so apps and users can call it.
Monitor in the Wild
Once live you monitor it: watch accuracy, latency, and incoming data so you notice trouble before your users do.
Retrain to Refresh
When the world drifts you retrain on fresh data, then loop back through evaluate and deploy. The cycle never truly ends.
Feedback Closes It
Real outcomes flow back as new labeled data. This feedback loop is what turns a one-shot model into a system that improves over time.
Each Stage Has Tools
Every stage has its own tooling: DVC for data, MLflow for training, FastAPI for serving. The lifecycle is your map to all of them.
Most Time Is Not Training
Surprisingly, training is the small part. Teams spend most effort on data prep and the operations around the model, not the fitting itself.
Automate the Turn
The MLOps goal is to automate each handoff so the loop can turn with little manual effort, from new data all the way to a new live model.
Quick Check
Let us check that the lifecycle loop is clear in your mind.
Recap
Data, train, evaluate, deploy, monitor, retrain, then repeat. Hold this lifecycle in your head and every MLOps tool will find its place. 🗺️
Frequently asked questions
Is the “The ML Lifecycle in One Picture” lesson free?
Yes — the full text of “The ML Lifecycle in One Picture” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “The ML Lifecycle in One Picture”?
Data, train, evaluate, deploy, monitor, retrain as a loop. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “The ML Lifecycle in One Picture” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- MLOps vs Plain Machine Learning
- The ML Lifecycle in One Picture
- Who Does What: Roles in an ML Team
- Your MLOps Maturity Checklist