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.