Self-service ML platform architecture in MLOps - Step-by-Step Execution

Process Flow - Self-service ML platform architecture

User Access

↓

Self-service Portal

↓

Model Development Environment

↓

Automated Pipelines

↓

Model Registry & Versioning

↓

Deployment & Monitoring

↓

Feedback Loop

↩Back to Model Development Environment

Users access the platform via a portal, develop models, run automated pipelines, register models, deploy and monitor them, then use feedback to improve models.

Execution Sample

MLOps

User -> Portal -> Dev Env -> Pipelines -> Registry -> Deployment -> Monitoring -> Feedback

Shows the flow of a self-service ML platform from user access to feedback for continuous improvement.

Process Table

Step	Component	Action	Result/State Change
1	User Access	User logs into portal	User authenticated and authorized
2	Self-service Portal	User selects or creates ML project	Project workspace created or opened
3	Model Development Environment	User codes or uploads model	Model code available for pipeline
4	Automated Pipelines	Pipeline triggered for training and validation	Model trained and validated
5	Model Registry & Versioning	Model registered with version	Model stored with metadata
6	Deployment & Monitoring	Model deployed to production	Model serving live with monitoring enabled
7	Feedback Loop	Monitoring data collected	Feedback sent to development for improvements
8	End	Cycle repeats for continuous improvement	Platform ready for next iteration

💡 Process is continuous; feedback loops back to development for ongoing model improvement

Status Tracker

Component State	Start	After Step 2	After Step 4	After Step 6	Final
User Authentication	Not authenticated	Authenticated	Authenticated	Authenticated	Authenticated
Project Workspace	None	Created/Open	Active	Active	Active
Model Code	None	None	Trained	Deployed	Deployed
Model Version	None	None	None	Versioned	Versioned
Deployment Status	Not deployed	Not deployed	Not deployed	Live	Live
Monitoring Data	None	None	None	Collected	Collected

Key Moments - 3 Insights

Why does the process loop back from Feedback Loop to Model Development Environment?

Because monitoring feedback helps improve models continuously, as shown in step 7 and 8 of the execution_table.

What happens if the user is not authenticated at User Access?

The user cannot proceed to the portal or any further steps, stopping the flow at step 1 in the execution_table.

Why is model versioning important in the Model Registry step?

It tracks different model versions for reproducibility and rollback, as indicated in step 5 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of the model after step 4?

A Model registered with version

B Model trained and validated

C Model deployed to production

D User authenticated

💡 Hint

Check the 'Result/State Change' column at step 4 in the execution_table

At which step does the model become live and monitored?

A Step 6

B Step 5

C Step 3

D Step 7

💡 Hint

Look for 'Model deployed to production' in the execution_table

If the user is not authenticated, what happens to the process flow?

A Process continues to deployment

B Model is registered anyway

C Process stops at User Access

D Feedback loop starts

💡 Hint

Refer to step 1 in the execution_table and key_moments about authentication

Concept Snapshot

Self-service ML platform flow:
User Access -> Portal -> Model Dev -> Pipelines -> Registry -> Deployment -> Monitoring -> Feedback
Automates model lifecycle with user control
Feedback loop enables continuous improvement
Model versioning ensures traceability
Deployment includes monitoring for live health

Full Transcript

A self-service ML platform lets users access a portal to create or open projects. They develop models in an environment, then trigger automated pipelines to train and validate models. Models are registered with versions for tracking. Deployment puts models into production with monitoring enabled. Monitoring data feeds back to development for continuous improvement. The process loops continuously to improve model quality and reliability. User authentication is required to start. Model versioning helps track changes. Deployment step makes models live and monitored. Feedback loop ensures ongoing updates.