ML lifecycle stages in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the first stage in the ML lifecycle?

The first stage is Data Collection. This is where raw data is gathered from various sources to be used for training the machine learning model.

beginner

Explain the purpose of the Data Preparation stage in the ML lifecycle.

Data Preparation involves cleaning, transforming, and organizing the collected data so it is ready for training. This step ensures the data quality is good and suitable for the model.

beginner

What happens during the Model Training stage?

In Model Training, the machine learning algorithm learns patterns from the prepared data by adjusting its parameters to minimize errors.

beginner

Why is Model Evaluation important in the ML lifecycle?

Model Evaluation checks how well the trained model performs on new, unseen data. It helps decide if the model is good enough or needs improvement.

beginner

What is the role of Deployment in the ML lifecycle?

Deployment is when the trained and evaluated model is put into a real environment where it can make predictions on live data.

Which stage involves cleaning and organizing data before training?

A Data Preparation

B Model Training

C Deployment

D Data Collection

Data Preparation is the stage where data is cleaned and organized to be ready for training.

What is the main goal of Model Evaluation?

A To check model performance on new data

B To gather raw data

C To deploy the model

D To train the model

Model Evaluation tests how well the model performs on unseen data to ensure it works correctly.

During which stage is the model actually created by learning from data?

A Data Collection

B Model Evaluation

C Deployment

D Model Training

Model Training is when the algorithm learns patterns from the data to create the model.

What happens in the Deployment stage?

A Model is tested on new data

B Model is put into production to make predictions

C Data is collected

D Data is cleaned

Deployment means putting the model into a real environment to use it for predictions.

Which stage comes right after Data Collection?

A Model Training

B Deployment

C Data Preparation

D Model Evaluation

After collecting data, it must be prepared before training the model.

Describe the main stages of the ML lifecycle and their purpose.

Think about what happens from getting data to using the model in real life.

Why is it important to evaluate a machine learning model before deployment?

Consider the risks of using a model that is not tested well.

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