Automate Retraining Pipelines
From new data to redeployed model.
Automate Retraining Pipelines is a free Deep Learning Academy lesson on CoddyKit — lesson 4 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 Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Models Need Refreshing
Once drift appears, a model must learn from new data. Doing this by hand every time is slow and error-prone, so we build a pipeline. 🔄
What a Pipeline Is
A retraining pipeline is just a chain of automatic steps. Each stage runs in order, passing its output to the next with no human pressing buttons.
Stage One: Get Fresh Data
The pipeline starts by pulling the latest examples. This ingestion step gathers new records and versions them so the run stays reproducible.
def fetch_data():
return load_latest("prod_logs")Stage Two: Train
Next the pipeline trains a fresh model on the combined data. This training step reuses your loop but runs unattended on a schedule.
def train(data):
model = build_model()
return fit(model, data)Stage Three: Evaluate
Never ship blindly. An evaluation step scores the new model on a held-out set so you know it is genuinely better, not just newer.
score = evaluate(model, test_set)Gate on Quality
Promote a model only if it earns it. A simple gate compares the new score to the live one and blocks any regression from reaching users.
if score > current_score:
deploy(model)Trigger on a Schedule
Pipelines often run on a clock. A cron trigger fires nightly or weekly so retraining happens steadily without anyone remembering to start it.
0 3 * * 0 python pipeline.pyOr Trigger on Drift
Even smarter, let monitoring start the pipeline. When your drift check fires an alert, it kicks off retraining as an event, so you react fast.
Orchestrators Run It All
Tools like Airflow or Prefect connect the stages, retry failures, and show a clear graph of what ran, when, and whether it succeeded.
Close the Loop
A deployed model feeds new logs, monitoring spots drift, the pipeline retrains, and a better model ships. This self-healing loop is the goal of MLOps.
Automation Frees You
With the loop running, you stop firefighting and start improving. The system keeps models fresh while you focus on real experiments. 🚀
Quick Check
What stops a worse model from being deployed automatically?
Recap
You learned to automate retraining: fetch data, train, evaluate, gate on quality, trigger on schedule or drift, and close the MLOps loop. 🎉
Frequently asked questions
Is the “Automate Retraining Pipelines” lesson free?
Yes — the full text of “Automate Retraining Pipelines” is free to read here on the web, and the Deep Learning 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 Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Automate Retraining Pipelines”?
From new data to redeployed model. You practise Deep Learning 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 Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Automate Retraining Pipelines” 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 Deep Learning Academy lesson?
Yes. Every Deep Learning 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
- Track Experiments with Weights & Biases
- Version Data & Models
- Detect Data & Model Drift
- Automate Retraining Pipelines