Machine Learning General

Question 1

Use Amazon ____________ to accelerate File mode training jobs.

Answer 1

FSx for Lustre

Question 2

a set of variables that controls how a model is trained.

Answer 2

Hyperparameters

Question 3

Finds the best version of a model by automating the training job within the limits of the hyperparameters that you specified.

Answer 3

Automatic Model Tuning

Question 4

The process of using the trained model to make predictions.

Answer 4

Inference

Question 5

Downloads data into the SageMaker instance volume before model training commences.

Answer 5

File mode

Question 6

Directly stream data from Amazon S3 into the training algorithm container.

Answer 6

Pipe mode

Question 7

automates the process of building, tuning, and deploying machine learning models based on a tabular dataset (CSV or Parquet).

Answer 7

SageMaker AutoPilot

Question 8

a data labeling service that lets you use workforce (human annotators) through your own private annotators, Amazon Mechanical Turk, or third-party services.

Answer 8

SageMaker GroundTruth

Question 9

a visual data preparation and cleaning tool that allows data scientists and engineers to easily clean and prepare data for machine learning.

Answer 9

SageMaker Data Wrangler

Question 10

allows you to optimize machine learning models for deployment on edge devices to run faster with no loss in accuracy.

Answer 10

SageMaker Neo

Question 11

automates the process of hyperparameter tuning based on the algorithm and hyperparameter ranges you specify. This can result in saving a significant amount of time for data scientists and engineers.

Answer 11

SageMaker Automatic Model Tuning

Question 12

provides real-time insights into the training process of machine learning models, enabling rapid iteration. It allows you to monitor and debug training issues, optimize model performance, and improve overall accuracy by analyzing various model-related metrics, such as weights, gradients, and biases.

Answer 12

Amazon SageMaker Debugger

Question 13

allows data scientists and engineers to save up to 90% on the cost of training machine learning models by using spare compute capacity.

Answer 13

Managed Spot Training

Question 14

allows for splitting the data and distributing the workload across multiple instances, improving speed and performance. It supports various distributed training frameworks such as TensorFlow, PyTorch, and MXNet.

Answer 14

Distributed Training