No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Predict Single and Batch Dataset

The video tutorial demonstrates using Amazon SageMaker Canvas for machine learning without coding. It covers creating a model with high accuracy, performing single and batch predictions, and evaluating results. The tutorial highlights the ease of use for those without a technical background, emphasizing the tool's efficiency and accuracy in predictions.

What accuracy did the model achieve according to the introduction?

• 95%

• 85%

• 98%

• 90%

What percentage of the dataset was used for training in the single prediction process?

• 80%

• 70%

• 50%

• 60%

What is the purpose of removing the output from the test dataset?

• To reduce dataset size

• To allow the model to predict

• To prevent overfitting

• To increase accuracy

What tool is used to perform batch predictions in the video?

• Amazon SageMaker Neo

• Amazon SageMaker Studio

• Amazon SageMaker Canvas

• Amazon SageMaker Ground Truth

What can be downloaded after generating batch predictions?

• Only predictions

• Only probabilities

• All values including predictions

• Only the test dataset

What is a key benefit of using SageMaker Canvas as highlighted in the video?

• It allows machine learning without coding

• It is only for experienced data scientists

• It requires extensive coding knowledge

• It is a costly tool

Who would benefit most from using SageMaker Canvas according to the video?

• Experienced programmers

• Data scientists with coding skills

• Software engineers

• Non-coders interested in machine learning