Automated model validation before promotion in MLOps - Mini Project: Build & Apply

Automated Model Validation Before Promotion

📖 Scenario: You work in a machine learning team. Before a new model version is promoted to production, it must pass automated validation checks. This ensures only good models are used in real applications.Imagine you have model performance scores from tests. You want to automatically check if the model meets quality standards before promotion.

🎯 Goal: Build a simple Python script that stores model test scores, sets a quality threshold, checks which models pass the threshold, and prints the list of models ready for promotion.

📋 What You'll Learn

Create a dictionary with model names and their accuracy scores

Add a variable for the minimum accuracy threshold

Use a dictionary comprehension to select models with accuracy above the threshold

Print the dictionary of models that passed validation

💡 Why This Matters

🌍 Real World

In real machine learning projects, automated validation scripts help teams quickly check if new models meet quality standards before deploying them to users.

💼 Career

Understanding automated model validation is key for roles in MLOps, data engineering, and machine learning engineering to ensure reliable and safe model deployment.

1

Create model accuracy data

Create a dictionary called model_scores with these exact entries: 'model_v1': 0.82, 'model_v2': 0.76, 'model_v3': 0.91, 'model_v4': 0.68

MLOps

# Create the model_scores dictionary with given model names and accuracy scores
# Your code here

Need a hint?

Use curly braces to create a dictionary. Each entry has a model name as a string key and a float accuracy value.

2

Set accuracy threshold

Add a variable called accuracy_threshold and set it to 0.80

MLOps

model_scores = {'model_v1': 0.82, 'model_v2': 0.76, 'model_v3': 0.91, 'model_v4': 0.68}
# Set the accuracy_threshold variable to 0.80
# Your code here

Need a hint?

Just assign the value 0.80 to the variable accuracy_threshold.

3

Select models passing threshold

Use a dictionary comprehension to create a new dictionary called validated_models that includes only models from model_scores with accuracy greater than accuracy_threshold

MLOps

model_scores = {'model_v1': 0.82, 'model_v2': 0.76, 'model_v3': 0.91, 'model_v4': 0.68}
accuracy_threshold = 0.80
# Create validated_models dictionary with models having accuracy > accuracy_threshold
# Your code here

Need a hint?

Use dictionary comprehension syntax: {key: value for key, value in dict.items() if condition}.

4

Print validated models

Write a print statement to display the validated_models dictionary

MLOps

model_scores = {'model_v1': 0.82, 'model_v2': 0.76, 'model_v3': 0.91, 'model_v4': 0.68}
accuracy_threshold = 0.80
validated_models = {model: score for model, score in model_scores.items() if score > accuracy_threshold}
# Print the validated_models dictionary
# Your code here

Need a hint?

Use print(validated_models) to show the dictionary of models that passed validation.