Domain 1, Data Engineering for Machine Learning
Obtaining large specialised datasets is a must to experiment and train Machine Learning models so they can recognise patterns in real world data and infer a prediction. Datasets can also be used as a source of labeled data to train models to generalise unlabeled real world data. Fortunately there are many data sources for datasets…
The Kinesis Producer Library (KPL) and the Kinesis API can both be used to send data to Kinesis Data Streams. The advantage of the KPL is it provides a lot of added features, such as failed transmission handling built in. If you use the Kinesis API you have to code these features yourself. The advantages…
This is a summary of the AWS Power Machine Learning at Scale White Paper which is a 15 page pdf document focusing on High Power Computing (HPC) in AWS. It can be downloaded from here: The list of White Papers for Machine Learning is on the Prepare for Your AWS Certification Exam web page: AWS…
These revision notes describe the AWS services available for storing data in data repositories for use in Machine Learning
Streaming data processing is used when data is continuously being generated and needs to be processed as it arrives. The AWS service for data streaming processing is Kinesis. Kineses comprises of four services each with different capabilities and some that can be used together. As well as Kinesis there is another AWS service that can…
These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
This Study Guide is about transforming raw data so it is ready for Machine Learning. There are two types of transformation: Identify and implement a data-transformation solution is sub-domain 1.3 of the Data Engineering knowledge domain. For more information about the exam structure see: AWS Machine Learning exam syllabus Questions To confirm your understanding scroll to…
For Machine Learning AWS glue and AWS Database Migration Service are used to ingest data. Batch processing refers to processing usually performed to a specific schedule. Before the batch process starts data is waiting and often any new data will have to wait for the next batch processing to be processed. In AWS any compute…
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