Self-service ML platform architecture in MLOps - Mini Project: Build & Apply

Build a Simple Self-Service ML Platform Architecture

📖 Scenario: You are part of a team building a self-service machine learning platform. This platform allows data scientists to upload datasets, configure training jobs, and deploy models without deep DevOps knowledge.To start, you will create a simple architecture representation using Python dictionaries to model components and their connections.

🎯 Goal: Build a basic data structure that represents the main components of a self-service ML platform and their relationships. Then, add configuration details, implement logic to find connected components, and finally display the architecture connections.

📋 What You'll Learn

Create a dictionary representing platform components and their connections

Add a configuration variable for maximum allowed connections

Write logic to filter components based on connection count

Print the filtered components and their connections

💡 Why This Matters

🌍 Real World

ML platforms help data scientists deploy models quickly without managing infrastructure details.

💼 Career

Understanding platform architecture and configuration is key for ML engineers and MLOps specialists to build scalable, user-friendly systems.

1

Create the platform components dictionary

Create a dictionary called ml_platform with these exact entries: 'Data Ingestion': ['Data Storage'], 'Data Storage': ['Feature Store', 'Model Training'], 'Feature Store': ['Model Training'], 'Model Training': ['Model Registry'], 'Model Registry': ['Model Deployment'], 'Model Deployment': [].

MLOps

# Create the ml_platform dictionary with components and their connections
# Your code here

Hint

Think of each key as a component and the list as components it connects to.

2

Add a maximum connections configuration

Add a variable called max_connections and set it to 2 to limit the number of connections a component can have.

MLOps

ml_platform = {
    'Data Ingestion': ['Data Storage'],
    'Data Storage': ['Feature Store', 'Model Training'],
    'Feature Store': ['Model Training'],
    'Model Training': ['Model Registry'],
    'Model Registry': ['Model Deployment'],
    'Model Deployment': []
}
# Add max_connections variable below
# Your code here

Hint

This variable will help filter components later.

3

Filter components by connection count

Create a dictionary called filtered_components that includes only components from ml_platform whose number of connections is less than or equal to max_connections. Use a dictionary comprehension with variables component and connections.

MLOps

ml_platform = {
    'Data Ingestion': ['Data Storage'],
    'Data Storage': ['Feature Store', 'Model Training'],
    'Feature Store': ['Model Training'],
    'Model Training': ['Model Registry'],
    'Model Registry': ['Model Deployment'],
    'Model Deployment': []
}
max_connections = 2
# Create filtered_components dictionary below
# Your code here

Hint

Use len(connections) <= max_connections inside the comprehension.

4

Display the filtered platform architecture

Write a print statement to display the filtered_components dictionary.

MLOps

ml_platform = {
    'Data Ingestion': ['Data Storage'],
    'Data Storage': ['Feature Store', 'Model Training'],
    'Feature Store': ['Model Training'],
    'Model Training': ['Model Registry'],
    'Model Registry': ['Model Deployment'],
    'Model Deployment': []
}
max_connections = 2
filtered_components = {component: connections for component, connections in ml_platform.items() if len(connections) <= max_connections}
# Print the filtered_components dictionary below
# Your code here

Hint

The output should show all components because all have 2 or fewer connections.

Practice

1. What is the main purpose of a self-service ML platform in an organization?

easy

A. To monitor only the hardware usage of ML servers

B. To replace data scientists with automated tools

C. To enable teams to build and deploy ML models independently and faster

D. To store large amounts of raw data without processing

Solution

1. Step 1: Understand the role of self-service ML platforms

   These platforms are designed to help teams work faster and independently by providing tools and interfaces for ML tasks.

2. Step 2: Compare options with this purpose

   Options A, B, and C do not focus on enabling teams to build and deploy models independently.

3. Final Answer:

   To enable teams to build and deploy ML models independently and faster -> Option C

4. Quick Check:

   Self-service ML platform purpose = Enable independent, faster ML work [OK]

Hint: Focus on independence and speed for ML teams [OK]

Common Mistakes:

• Confusing data storage with platform purpose
• Thinking it replaces data scientists
• Assuming it only monitors hardware

2. Which component is essential in a self-service ML platform for managing model versions?

easy

A. Model registry

B. Data ingestion pipeline

C. Experiment tracking UI

D. Security gateway

Solution

1. Step 1: Identify the component for model version management

   The model registry is designed to store and manage different versions of ML models.

2. Step 2: Eliminate other options

   Data ingestion handles data, experiment tracking logs experiments, and security gateway manages access, none manage model versions.

3. Final Answer:

   Model registry -> Option A

4. Quick Check:

   Model version management = Model registry [OK]

Hint: Model versions live in the registry, not data or security parts [OK]

Common Mistakes:

• Confusing experiment tracking with model versioning
• Choosing data pipeline for model management
• Mixing security with model storage

3. Given a self-service ML platform with components: UI, data pipeline, model registry, deployment, and monitoring, which sequence correctly represents the typical workflow?

medium

A. UI -> Data pipeline -> Model registry -> Deployment -> Monitoring

B. Data pipeline -> Model registry -> UI -> Deployment -> Monitoring

C. Data pipeline -> UI -> Model registry -> Deployment -> Monitoring

D. UI -> Model registry -> Data pipeline -> Deployment -> Monitoring

Solution

1. Step 1: Understand the typical ML workflow in a self-service platform

   The user interacts with the UI first to start tasks, then data is processed, models are registered, deployed, and monitored.

2. Step 2: Match the sequence with this logic

   UI -> Data pipeline -> Model registry -> Deployment -> Monitoring starts with UI, then data pipeline, model registry, deployment, and monitoring, which fits the workflow.

3. Final Answer:

   UI -> Data pipeline -> Model registry -> Deployment -> Monitoring -> Option A

4. Quick Check:

   Workflow order = UI first, then data, model, deploy, monitor [OK]

Hint: User starts at UI, then data, model, deploy, monitor [OK]

Common Mistakes:

• Starting workflow with data pipeline instead of UI
• Mixing order of model registry and UI
• Placing data pipeline after deployment

4. A self-service ML platform's deployment component fails to update models after new versions are registered. What is the most likely cause?

medium

A. The data pipeline is processing data too slowly

B. The model registry is not linked to the deployment pipeline

C. The UI does not allow model version selection

D. Monitoring tools are not configured

Solution

1. Step 1: Analyze the failure symptom

   Deployment does not update models after new versions are registered, indicating a disconnect between model registry and deployment.

2. Step 2: Evaluate options for cause

   Slow data pipeline or UI issues won't stop deployment updates; monitoring tools affect tracking, not deployment.

3. Final Answer:

   The model registry is not linked to the deployment pipeline -> Option B

4. Quick Check:

   Deployment update failure = Missing link to model registry [OK]

Hint: Check if deployment connects to model registry for updates [OK]

Common Mistakes:

• Blaming data pipeline speed for deployment issues
• Assuming UI controls deployment updates
• Confusing monitoring with deployment functionality

5. You want to design a self-service ML platform that allows data scientists to run experiments, register models, deploy them, and monitor performance with minimal manual steps. Which architectural feature best supports this goal?

hard

A. Relying on external tools for monitoring without integration

B. Separating data ingestion and model deployment into isolated manual workflows

C. Using a UI that only displays model metrics without deployment controls

D. Integrating experiment tracking with automated model registration and deployment pipelines

Solution

1. Step 1: Identify the goal of minimal manual steps

   This requires automation and integration between experiment tracking, model registration, and deployment.

2. Step 2: Evaluate architectural options

   Integrating experiment tracking with automated model registration and deployment pipelines integrates these components with automation, supporting the goal. Options B, C, and D involve manual or disconnected steps.

3. Final Answer:

   Integrating experiment tracking with automated model registration and deployment pipelines -> Option D

4. Quick Check:

   Automation and integration = minimal manual steps [OK]

Hint: Automation and integration reduce manual work [OK]

Common Mistakes:

• Choosing isolated manual workflows
• Ignoring deployment controls in UI
• Using disconnected monitoring tools