ML lifecycle stages in MLOps - Step-by-Step Execution

Process Flow - ML lifecycle stages

Data Collection

↓

Data Preparation

↓

Model Training

↓

Model Evaluation

↓

Model Deployment

↓

Monitoring & Maintenance

↩Back to Data Collection (if needed)

The ML lifecycle moves step-by-step from gathering data to preparing it, training a model, checking its quality, deploying it, and then monitoring it for improvements.

Execution Sample

MLOps

Stage = 'Data Collection'
if Stage == 'Data Collection':
    Stage = 'Data Preparation'
# ... continues through stages

This code simulates moving through the ML lifecycle stages one by one.

Process Table

Step	Current Stage	Action	Next Stage
1	Data Collection	Collect raw data from sources	Data Preparation
2	Data Preparation	Clean and format data	Model Training
3	Model Training	Train model on prepared data	Model Evaluation
4	Model Evaluation	Test model accuracy and performance	Model Deployment
5	Model Deployment	Deploy model to production	Monitoring & Maintenance
6	Monitoring & Maintenance	Track model performance and update	Data Collection (if needed)
7	Data Collection	Cycle repeats if model needs retraining	Data Preparation

💡 Cycle repeats to improve model or stops if model performs well

Status Tracker

Stage	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7
Stage	Data Collection	Data Preparation	Model Training	Model Evaluation	Model Deployment	Monitoring & Maintenance	Data Collection	Data Preparation

Key Moments - 3 Insights

Why does the lifecycle return to Data Collection after Monitoring?

Because monitoring may show the model needs new or updated data, so the cycle restarts to improve the model, as seen in steps 6 and 7 of the execution_table.

Is Model Deployment the last step in the ML lifecycle?

No, after deployment, the model must be monitored and maintained to ensure it works well over time, shown in step 5 moving to step 6.

What happens during Data Preparation?

Raw data is cleaned and formatted to be usable for training, which is the action in step 2 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table, what is the stage after Model Training?

A Model Deployment

B Model Evaluation

C Data Preparation

D Monitoring & Maintenance

💡 Hint

Check row 3 in the execution_table under Next Stage column.

At which step does the lifecycle start monitoring the model?

A Step 6

B Step 5

C Step 4

D Step 7

💡 Hint

Look at the Current Stage column in step 6 of the execution_table.

If the model performs well, what happens to the cycle?

A It skips Monitoring & Maintenance

B It repeats from Data Collection

C It stops after Model Deployment

D It jumps directly to Model Training

💡 Hint

Refer to the exit_note explaining when the cycle stops.

Concept Snapshot

ML lifecycle stages:
1. Data Collection - gather raw data
2. Data Preparation - clean and format data
3. Model Training - build model
4. Model Evaluation - test model
5. Model Deployment - release model
6. Monitoring & Maintenance - track and update
Cycle repeats if needed to improve model

Full Transcript

The ML lifecycle starts with collecting data, then preparing it for use. Next, a model is trained on this data. After training, the model is evaluated to check its quality. If good, it is deployed to production. Then, the model is monitored to ensure it keeps working well. If monitoring shows problems or new data is available, the cycle restarts from data collection to improve the model. This cycle helps keep ML models accurate and useful over time.

Practice

1. Which stage in the ML lifecycle involves collecting and preparing data for training?

easy

A. Model Training

B. Data Preparation

C. Model Monitoring

D. Model Deployment

Solution

1. Step 1: Understand the role of data in ML lifecycle

   Data must be collected and cleaned before training a model.

2. Step 2: Identify the stage focused on data tasks

   Data Preparation is the stage where data is gathered and made ready for training.

3. Final Answer:

   Data Preparation -> Option B

4. Quick Check:

   Data Preparation = Collecting and cleaning data [OK]

Hint: Data tasks happen before training starts [OK]

Common Mistakes:

• Confusing deployment with data tasks
• Thinking monitoring includes data cleaning
• Mixing training with data preparation

2. Which of the following is the correct order of stages in a typical ML lifecycle?

easy

A. Data Preparation -> Model Training -> Model Deployment -> Model Monitoring

B. Model Deployment -> Model Training -> Data Preparation -> Model Monitoring

C. Model Training -> Data Preparation -> Model Deployment -> Model Monitoring

D. Model Monitoring -> Model Deployment -> Model Training -> Data Preparation

Solution

1. Step 1: Recall the logical flow of ML lifecycle stages

   First, data is prepared, then the model is trained, followed by deployment and monitoring.

2. Step 2: Match the correct sequence from options

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring correctly lists the stages in order: Data Preparation -> Model Training -> Model Deployment -> Model Monitoring.

3. Final Answer:

   Data Preparation -> Model Training -> Model Deployment -> Model Monitoring -> Option A

4. Quick Check:

   Correct stage order = Data Preparation -> Model Training -> Model Deployment -> Model Monitoring [OK]

Hint: Remember: Prepare data before training [OK]

Common Mistakes:

• Mixing deployment before training
• Starting with monitoring instead of data
• Incorrect stage order

3. Consider this simplified ML lifecycle code snippet:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
for i, stage in enumerate(stages):
    print(f"Stage {i+1}: {stage}")

What will be the output of this code?

medium

A. Stage 1: Model Training
Stage 2: Data Preparation
Stage 3: Model Deployment
Stage 4: Model Monitoring

B. Stage 0: Data Preparation
Stage 1: Model Training
Stage 2: Model Deployment
Stage 3: Model Monitoring

C. Stage 1: Data Preparation
Stage 2: Model Training
Stage 3: Model Deployment
Stage 4: Model Monitoring

D. Stage 1: Data Preparation
Stage 2: Model Deployment
Stage 3: Model Training
Stage 4: Model Monitoring

Solution

1. Step 1: Understand enumerate behavior in the loop

   enumerate(stages) gives index and value starting at 0, but print uses i+1 for stage number.

2. Step 2: Check the order of stages printed

   The loop prints stages in list order with stage numbers 1 to 4 matching the list order.

3. Final Answer:

   Stage 1: Data Preparation Stage 2: Model Training Stage 3: Model Deployment Stage 4: Model Monitoring -> Option C

4. Quick Check:

   Index + 1 matches stage number [OK]

Hint: Remember enumerate starts at 0, add 1 for display [OK]

Common Mistakes:

• Confusing index starting at 0
• Mixing stage order in output
• Printing wrong stage names

4. You have this ML lifecycle stage list:

stages = ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']
stages.remove('Model Training')
print(stages)

What is the output after running this code?

medium

A. ['Data Preparation', 'Model Deployment', 'Model Monitoring']

B. ['Model Training', 'Model Deployment', 'Model Monitoring']

C. ['Data Preparation', 'Model Training', 'Model Deployment', 'Model Monitoring']

D. Error: 'remove' method not found

Solution

1. Step 1: Understand what stages.remove('Model Training') does

   This removes the first occurrence of 'Model Training' from the list.

2. Step 2: Check the list after removal

   The list now excludes 'Model Training', leaving the other three stages.

3. Final Answer:

   ['Data Preparation', 'Model Deployment', 'Model Monitoring'] -> Option A

4. Quick Check:

   Remove deletes specified item from list [OK]

Hint: remove() deletes the exact item from list [OK]

Common Mistakes:

• Expecting an error from remove()
• Thinking remove deletes by index
• Not updating the list after removal

5. A team wants to automate retraining their ML model when data changes. Which two ML lifecycle stages must be combined in a pipeline to achieve this?

hard

A. Data Preparation and Model Monitoring

B. Model Deployment and Model Monitoring

C. Model Training and Model Deployment

D. Data Preparation and Model Training

Solution

1. Step 1: Identify stages involved in retraining after data changes

   Retraining requires fresh data preparation and then training the model again.

2. Step 2: Select stages that automate retraining

   Data Preparation and Model Training together form the pipeline for retraining.

3. Final Answer:

   Data Preparation and Model Training -> Option D

4. Quick Check:

   Retrain = Prepare data + Train model [OK]

Hint: Retraining needs fresh data prep plus training [OK]

Common Mistakes:

• Confusing deployment with retraining
• Thinking monitoring triggers retraining alone
• Ignoring data preparation before training