Open this lesson in your favourite AI. It'll walk you through the why, explain the demo, and quiz you on the try-it list.
Everything in this course is a station on one loop: data flows in, you experiment and train (or iterate prompts), you evaluate, you deploy, you observe in production, the production signals become new data, and you go around again. The defining property of a mature ML system is that this loop is closed and turns continuously — degradation is detected, fixes flow back through the same pipeline, and nothing requires a heroic manual rebuild. Seeing the whole loop before diving into each station is what keeps the next nine modules from feeling like disconnected tools. By the end you should be able to point at any production incident and say which station of the loop failed.
The demo represents the lifecycle as an explicit loop and shows that a production signal (drift detected) feeds back to the data/experiment station — proving the loop is a cycle, not a one-way pipeline.
Use these three in order. Each builds on the one before.
In one paragraph, describe the continuous ML lifecycle loop from data to deployment to feedback.
Walk me through each station of the MLOps loop and how a signal from production feeds back to become new data.
Given a production failure in my ML/LLM system, walk me through how to localize which station of the lifecycle loop broke and what to fix.
loop = ["data", "experiment/train", "evaluate", "deploy",
"observe", "feedback"]
# 'feedback' wraps back to 'data' -> the loop closes.
def next_station(current):
i = loop.index(current)
return loop[(i + 1) % len(loop)] # modulo makes it a CYCLE, not a line
s = "observe"
print("from", s, "->", next_station(s)) # observe -> feedback
print("from feedback ->", next_station("feedback")) # feedback -> data (loop closes)
# A production incident maps to the station that failed:
incidents = {"stale answers": "data", "regression after deploy": "evaluate",
"silent accuracy drop": "observe"}
for symptom, station in incidents.items():
print(f"'{symptom}' -> failed at the '{station}' station")python3 main.py