Multi-tenancy and isolation in MLOps - Mini Project: Build & Apply

Multi-tenancy and Isolation in MLOps

📖 Scenario: You are managing machine learning models for different teams in a company. Each team should only access their own models and data. This is called multi-tenancy with isolation.We will create a simple system to store model names by team and then retrieve only the models for a specific team.

🎯 Goal: Build a Python program that stores models for multiple teams in a dictionary, sets a team name as a filter, and then lists only the models belonging to that team.

📋 What You'll Learn

Create a dictionary called team_models with exact keys and values for teams and their models

Create a variable called current_team with the exact team name to filter

Use a list comprehension to create a list called filtered_models containing models only for current_team

Print the filtered_models list exactly as shown

💡 Why This Matters

🌍 Real World

In real MLOps, multiple teams share infrastructure. Multi-tenancy ensures each team accesses only their own models and data, preventing mix-ups and security issues.

💼 Career

Understanding multi-tenancy and isolation is key for roles managing shared ML platforms, ensuring data privacy and smooth collaboration.

1

Create the multi-tenant model storage

Create a dictionary called team_models with these exact entries: 'team_alpha': ['model1', 'model2'], 'team_beta': ['model3'], 'team_gamma': ['model4', 'model5', 'model6'].

MLOps

# Create the team_models dictionary with exact entries
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. Keys are team names as strings. Values are lists of model names.

2

Set the current team for isolation

Create a variable called current_team and set it to the string 'team_gamma' to select that team's models.

MLOps

team_models = {
    'team_alpha': ['model1', 'model2'],
    'team_beta': ['model3'],
    'team_gamma': ['model4', 'model5', 'model6']
}
# Set current_team to 'team_gamma'
# Your code here

Need a hint?

Assign the string 'team_gamma' to the variable current_team.

3

Filter models for the current team

Use a list comprehension to create a list called filtered_models that contains only the models from team_models[current_team].

MLOps

team_models = {
    'team_alpha': ['model1', 'model2'],
    'team_beta': ['model3'],
    'team_gamma': ['model4', 'model5', 'model6']
}

current_team = 'team_gamma'
# Create filtered_models list with models only for current_team
# Your code here

Need a hint?

Use a list comprehension to loop over team_models[current_team] and collect each model.

4

Display the filtered models

Write a print statement to display the filtered_models list exactly as it is.

MLOps

team_models = {
    'team_alpha': ['model1', 'model2'],
    'team_beta': ['model3'],
    'team_gamma': ['model4', 'model5', 'model6']
}

current_team = 'team_gamma'

filtered_models = [model for model in team_models[current_team]]
# Print the filtered_models list
# Your code here

Need a hint?

Use print(filtered_models) to show the list of models for the current team.