Developing Machine Learning Solutions

Question 1

End to end machine learning lifecycle process

Answer 1

Business goal identification

ML problem framing

Data processing (data collection, data preprocessing, and feature engineering)

Model development (training, tuning, and evaluation)

Model deployment (inference and prediction)

Model monitoring

Model retraining

Question 2

Bias

Answer 2

The gap between your predicted value and the actual value

Question 3

Variance

Answer 3

Dispersion of your predicted values

Question 4

Classification metrics

Answer 4

1) Accuracy 2) Precision 3) Recall 4) F1 4) AUC-ROC

Question 5

Regression metrics

Answer 5

1) Mean squared error
2) R squared

Question 6

Confusion matrix

Answer 6

For a classification model. A confusion matrix can help classify why and how a model gets something wrong.

Question 7

Model Accuracy or Score Equation

Answer 7

Correct predictions/Total number of predictions

Question 8

Precision equation

Answer 8

Positive predictions that are correct/Total number of positive predictions. Great metric when the cost of false positives is high

Question 9

Recall (sensitivity) equation

Answer 9

True positive count/sum of true positive and false negatives

Question 10

AUC-ROC

Answer 10

For classification models. show what the curve for true positive compared to false positive looks like at various thresholds. AUC-ROC uses sensitivity (true positive rate) and specificity (false positive rate)

Question 11

What is feature engineering?

Answer 11

Feature engineering transforms data into features or inputs that will be valuable for the model.

Question 12

Machine learning solution without code

Answer 12

SageMaker Canvas

Question 13

What is MLOps

Answer 13

MLOps is a set of practices and principles that aims to manage the entire lifecycle of machine learning systems, from model development and training to deployment, monitoring, and maintenance. It provides a structured approach to streamlining the ML workflow, ensuring reliability, scalability, and reproducibility.

Question 14

What is the machine learning lifecycle?

Answer 14

the end-to-end process of developing, deploying, and maintaining machine learning models from identifying the business problem to the deploying and monitoring the model.