What is MLOps - Visual Explanation

Process Flow - What is MLOps

Start: Data Scientist builds ML Model

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Train Model with Data

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Test Model Performance

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Package Model for Deployment

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Deploy Model to Production

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Monitor Model in Production

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Collect Feedback & Data

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Improve Model & Repeat Cycle

MLOps is a cycle that helps teams build, deploy, and improve machine learning models smoothly and reliably.

Execution Sample

MLOps

1. Collect data
2. Train model
3. Test model
4. Deploy model
5. Monitor model
6. Update model

This shows the main steps in MLOps from data collection to model updating.

Process Table

Step	Action	Result	Next Step
1	Collect data	Data ready for training	Train model
2	Train model	Model learns patterns	Test model
3	Test model	Model accuracy measured	Deploy model if good
4	Deploy model	Model available to users	Monitor model
5	Monitor model	Track model performance	Collect feedback
6	Collect feedback	New data and errors found	Improve model
7	Improve model	Model updated and better	Repeat cycle

💡 Cycle repeats to keep model accurate and useful

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
Data	Raw data	Cleaned and ready	Used for training	Used for testing	Used in production	Monitored for quality	Updated with feedback	Improved data
Model	None	Training started	Trained model	Tested model	Deployed model	Monitored model	Improved model	Updated model

Key Moments - 3 Insights

Why do we need to monitor the model after deployment?

Monitoring ensures the model works well with real users and data, as shown in step 5 of the execution table.

What happens if the model performs poorly during testing?

If testing shows poor accuracy (step 3), the model should be improved before deployment, not deployed yet.

Why is MLOps a cycle and not a one-time process?

Because data and conditions change, the model needs continuous updates and improvements, as the cycle repeats after step 7.

Visual Quiz - 3 Questions

Look at the execution table, what is the result after step 4?

A Data ready for training

B Model available to users

C Model accuracy measured

D New data and errors found

💡 Hint

Check the 'Result' column for step 4 in the execution table.

At which step does the model get tested for accuracy?

A Step 3

B Step 2

C Step 5

D Step 6

💡 Hint

Look at the 'Action' column in the execution table to find testing.

If monitoring finds problems, which step happens next?

A Deploy model

B Train model

C Collect feedback

D Test model

💡 Hint

See what follows 'Monitor model' in the execution table.

Concept Snapshot

MLOps is a cycle for managing ML models:
1. Collect data
2. Train model
3. Test model
4. Deploy model
5. Monitor model
6. Collect feedback
7. Improve model and repeat
It keeps ML models reliable and updated.

Full Transcript

MLOps is a process that helps teams build and manage machine learning models. It starts with collecting data, then training the model to learn from that data. After training, the model is tested to check how well it works. If the model performs well, it is deployed so users can use it. Once deployed, the model is monitored to make sure it keeps working well. Feedback and new data are collected to find any problems or improvements. The model is then updated and improved, and the cycle repeats to keep the model accurate and useful over time.