MLS-C01 Exam Dumps - AWS Certified Machine Learning - Specialty

A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data1. For multi-class classification, the matrix shape will be equal to the number of classes i.e for n classes it will be nXn1. The diagonal values represent the number of correct predictions for each class, and the off-diagonal values represent the number of incorrect predictions for each class1.

The BlazingText algorithm is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). BlazingText works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values2.

From the confusion matrices for the training and test sets, we can observe the following:

The model has a high accuracy on the training set, as most of the diagonal values are high and the off-diagonal values are low. This means that the model is able to learn the patterns and features of the training data well.

However, the model has a lower accuracy on the test set, as some of the diagonal values are lower and some of the off-diagonal values are higher. This means that the model is not able to generalize well to the unseen data and makes more errors.

The model has a particularly high error rate for classes B and E on the test set, as the values of M_22 and M_55 are much lower than the values of M_12, M_21, M_15, M_25, M_51, and M_52. This means that the model is confusing classes B and E with other classes more often than it should.

The model has a relatively low error rate for classes A, C, and D on the test set, as the values of M_11, M_33, and M_44 are high and the values of M_13, M_14, M_23, M_24, M_31, M_32, M_34, M_41, M_42, and M_43 are low. This means that the model is able to distinguish classes A, C, and D from other classes well.

These results indicate that the model is overfitting for classes B and E, meaning that it is memorizing the specific features of these classes in the training data, but failing to capture the general features that are applicable to the test data. Overfitting is a common problem in machine learning, where the model performs well on the training data, but poorly on the test data3. Some possible causes of overfitting are:

The model is too complex or has too many parameters for the given data. This makes the model flexible enough to fit the noise and outliers in the training data, but reduces its ability to generalize to new data.

The data is too small or not representative of the population. This makes the model learn from a limited or biased sample of data, but fails to capture the variability and diversity of the population.

The data is imbalanced or skewed. This makes the model learn from a disproportionate or uneven distribution of data, but fails to account for the minority or rare classes.

Some possible solutions to prevent or reduce overfitting are:

Simplify the model or use regularization techniques. This reduces the complexity or the number of parameters of the model, and prevents it from fitting the noise and outliers in the data. Regularization techniques, such as L1 or L2 regularization, add a penalty term to the loss function of the model, which shrinks the weights of the model and reduces overfitting3.

Increase the size or diversity of the data. This provides more information and examples for the model to learn from, and increases its ability to generalize to new data. Data augmentation techniques, such as rotation, flipping, cropping, or noise addition, can generate new data from the existing data by applying some transformations3.

Balance or resample the data. This adjusts the distribution or the frequency of the data, and ensures that the model learns from all classes equally. Resampling techniques, such as oversampling or undersampling, can create a balanced dataset by increasing or decreasing the number of samples for each class3.

Confusion Matrix in Machine Learning - GeeksforGeeks

BlazingText algorithm - Amazon SageMaker

Overfitting and Underfitting in Machine Learning - GeeksforGeeks

To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container’s file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances. References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker

The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:

Add more data to the training set and retrain the model using transfer learning to reduce the bias: Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.

Transfer learning for TensorFlow image classification models in Amazon SageMaker

Transfer learning for custom labels using a TensorFlow container and “bring your own algorithm” in Amazon SageMaker

Machine Learning Concepts - AWS Training and Certification

 The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:

Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.

Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.

Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.

Deep Learning - Machine Learning Lens

How to Avoid Overfitting in Deep Learning Neural Networks

How to Identify Overfitting Machine Learning Models in Scikit-Learn

The SageMaker image classification algorithm supports both RecordIO and image content types for training in file mode, and supports the RecordIO content type for training in pipe mode1. However, the algorithm also supports training in pipe mode using the image files without creating RecordIO files, by using the augmented manifest format2. In this case, the company should generate

The best solution for this scenario is to set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination, and use the built-in transforms Filter, Map, and RenameField to perform the required transformations. This solution has the following advantages:

It minimizes the effort that is needed to set up infrastructure for the ingestion and transformation, as AWS Glue is a fully managed service that provides a serverless Apache Spark environment, a graphical interface to define data sources and targets, and a code generation feature to create and edit scripts1.

It automates the extraction and transformation process, as AWS Glue can schedule the job to run monthly, and handle the connection, authentication, and configuration of the Amazon Redshift cluster and the S3 bucket2.

It minimizes the load on the Amazon Redshift cluster, as AWS Glue can read the data from the cluster in parallel and use a JDBC connection that supports SSL encryption3.

It performs the required transformations, as AWS Glue can use the built-in transforms Filter, Map, and RenameField to remove the rows with NULL values, split the timestamp column into date and time columns, and rename the card name column, respectively4.

The other solutions are not optimal or suitable, because they have the following drawbacks:

A: Setting up an Amazon EMR cluster and creating an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data is not the most efficient or convenient solution, as it requires more effort and resources to provision, configure, and manage the EMR cluster, and to write and maintain the Spark code5.

B: Setting up an Amazon EC2 instance with a SQL client tool to query the data from the Amazon Redshift cluster directly and export the resulting dataset into a CSV file is not a scalable or reliable solution, as it depends on the availability and performance of the EC2 instance, and the manual execution and upload of the SQL queries and the CSV file6.

D: Using Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket and creating an AWS Lambda function to run the query monthly is not a feasible solution, as Amazon Redshift Spectrum does not support writing data to external tables or S3 buckets, only reading data from them7.

1: What Is AWS Glue? - AWS Glue

2: Populating the Data Catalog - AWS Glue

3: Best Practices When Using AWS Glue with Amazon Redshift - AWS Glue

4: Built-In Transforms - AWS Glue

5: What Is Amazon EMR? - Amazon EMR

6: Amazon EC2 - Amazon Web Services (AWS)

7: Using Amazon Redshift Spectrum to Query External Data - Amazon Redshift

The best way to visualize the model’s degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model’s performance. References:

Histogram - Wikipedia

Model error - Wikipedia

SageMaker Model Monitor - visualizing monitoring results

To prepare and transform historical data efficiently with minimal setup, Amazon SageMaker Data Wrangler is the optimal tool. Data Wrangler simplifies data preprocessing and exploratory data analysis (EDA) by providing a graphical interface for transformations and insights. By first uploading the CSV data to Amazon S3, the data becomes easily accessible to SageMaker and can be imported directly into Data Wrangler.

Once in Data Wrangler, the team can perform required data transformations and generate insights in a single workflow, avoiding the need for additional tools like Amazon QuickSight or further notebook configuration. This approach provides the simplest and most integrated solution for the data science team.