AI in Industry: your first working example

Why this example matters

A small sequence of vibration readings stands in for machine condition monitoring. A threshold identifies a possible anomaly that might justify inspection.

What you will be able to do

• Recognize the input values and describe what they represent in AI in Industry.
• Run the AI in Industry program and explain why its displayed output follows from the recorded operations.
• Identify a limitation of the AI in Industry activity before applying similar reasoning to a real system.

Original worked program

The following AI in Industry demonstration uses small invented values so you can trace every operation. The AI in Industry activity uses only Python standard-library features; it downloads no model and calls no paid service.

machine_vibration = [2.1, 2.3, 4.8, 2.4]
alert_level = 4.0
alerts = [index + 1 for index, value in enumerate(machine_vibration) if value > alert_level]
print("Alert at readings:", alerts)
print("Total alerts:", len(alerts))

Actual output from Python

Alert at readings: [3]
Total alerts: 1

Read each instruction

1. machine_vibration = [2.1, 2.3, 4.8, 2.4]The assignment gives a name to a value or a calculated result in AI in Industry. In the AI in Industry activity, check the named values on that line rather than memorizing symbols.
2. alert_level = 4.0The assignment gives a name to a value or a calculated result in AI in Industry. In the AI in Industry activity, check the named values on that line rather than memorizing symbols.
3. alerts = [index + 1 for index, value in enumerate(machine_vibration) if value > alert_level]The assignment gives a name to a value or a calculated result in AI in Industry. In the AI in Industry activity, check the named values on that line rather than memorizing symbols.

Observe the results

To verify the AI in Industry result, copy the program into a fresh file and run it once without modification. Then change only one input. For the AI in Industry trial, record what you predict before pressing Run again. Compare the AI in Industry prediction with the observed result.

Second original practice: Map process inputs and outputs

The AI in Industry extension changes the task, not just the numbers in the first example. Study its new data structure or operation, then decide how the result supports the milestone “Map process inputs and outputs”.

before_errors = 24
after_errors = 15
improvement = (before_errors-after_errors)/before_errors
print("Error reduction:", f"{improvement:.1%}")

Actual second output

Error reduction: 37.5%

For the AI in Industry extension, read the code from its first assignment or import to its final print statement. Explain the additional AI in Industry idea introduced by this extension, then design a check that could reveal a mistake. The AI in Industry extension output above was recorded by running that independent program.

What the example does not prove

A single high reading does not prove equipment failure. Sensor calibration, operating conditions and maintenance history matter before changing a physical process.

A reliable AI in Industry project also needs an explicit objective, test cases that were not used to tune the approach, documentation of the assumptions, and review of situations where the result might mislead the user. Do not mistake the small AI in Industry program for evidence of accuracy or safety in a deployed application.

How to validate your own result

For AI in Industry, a convincing result starts with a written claim about the actual task, not the appearance of a successful print statement. For AI in Industry, list the initial values and the one value you will vary; record a prediction that could be disproved. Save both AI in Industry outputs as separate records to avoid mixing up the results of the two investigations. A correct Python run only establishes that the particular instructions executed; it does not establish that the assumptions match every situation in which AI in Industry might be used.

Connect this trial to “Choose actionable metrics” without claiming that the milestone is already complete. Name a measurement you would collect for AI in Industry, a plausible error source and a person able to review the decision. The later topic “Quality assurance data” deserves its own investigation with different evidence. Keeping these questions separate prevents your AI in Industry learning journal from becoming a collection of exercises that all make the same claim.

Finally, keep the AI in Industry experiment reproducible: include the Python version, exact script, input values, observed results and any unexpected behavior. If a AI in Industry investigation uses external data, document the source and your permission to use those records. A new learner should be able to explain every output line in ordinary words before moving into the next milestone of AI in Industry.

Second verified lab: Data in practice

To deepen Chapter 1 of AI in Industry, compare the first calculation with a different, executable experiment. The small data lab below illustrates a related technique, not every possible implementation of frame a business problem.

# Course: AI in Industry | Chapter 1 | Subject: Frame a business problem
# Validate a toy input table before using it downstream
records=[{"id":1,"value":11},{"id":2,"value":None},{"id":3,"value":3}]
valid=[row for row in records if isinstance(row["value"],int)]
print("Rows received:",len(records))
print("Valid rows:",len(valid))
print("Missing rows:",len(records)-len(valid))
print("Valid total:",sum(row["value"] for row in valid))

Actual output of the displayed code

Rows received: 3
Valid rows: 2
Missing rows: 1
Valid total: 14

While examining your first working example in the ai in industry course (chapter 1), apply this test: Explicit schema and missing-value checks make the transformation reproducible. The example does not silently treat missing observations as zeros. For the warehouse package inspections exercise, distinguish what the code observes from any stronger predictive or causal claim.

While examining your first working example in the ai in industry course (chapter 1), apply this test: Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.

20 practice questions and worked answers

Test your understanding of your first working example in the ai in industry course (chapter 1). Compare the claims in both runnable labs, and use the outputs to defend each numerical response.

Build one original mini-project

Chapter 1 checkpoint: Explain how frame a business problem affects the warehouse package inspections example; demonstrate one failing input; then propose a revision with an acceptance test and a recorded output. This checkpoint belongs to the AI in Industry course.

Propose a low-risk AI pilot for a warehouse, naming the human who decides whether to act on each warning.

A six-step practice plan

1. Frame a business problem. For the “Frame a business problem” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.
2. Map process inputs and outputs. For the “Map process inputs and outputs” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.
3. Choose actionable metrics. For the “Choose actionable metrics” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.
4. Quality assurance data. For the “Quality assurance data” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.
5. Predictive maintenance signals. For the “Predictive maintenance signals” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.
6. Supply chain forecasting. For the “Supply chain forecasting” exercise, identify one observable result and record what would demonstrate progress in AI in Industry.

Save your original inputs, your changed inputs, the two actual outputs and a short explanation of what you learned about AI in Industry. The AI in Industry record forms evidence for the first milestone of your project portfolio.

Where you go next

Your AI in Industry roadmap has 18 subject-specific milestones progressing toward evaluation and a capstone. Future AI in Industry lessons will require new demonstrations and evidence, beyond the skills taught in this first chapter.