Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company’s customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers. Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.

Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog

How to Apply Machine Learning to Customer Feedback

The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.

The other solutions are less optimal because they require more development effort and additional services. Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput. Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.

Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use built-in functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.

Therefore, the company can use the following architecture to build the near-real time defect detection solution:

Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations: Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.

Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection: The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.

Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.

What Is Amazon Kinesis Data Firehose?

What Is Amazon Kinesis Data Analytics for SQL Applications?

DeepAR Forecasting Algorithm - Amazon SageMaker