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Often the Data Team, comprising Data and MLEngineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier. What is an ETL data pipeline in ML? Let’s look at the importance of ETL pipelines in detail.
This situation is not different in the ML world. Data Scientists and MLEngineers typically write lots and lots of code. Building a mental model for ETL components Learn the art of constructing a mental representation of the components within an ETL process.
To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. We can analyze activities by identifying stops made by the user or mobile device by clustering pings using ML models in Amazon SageMaker.
The customer used this pipeline for small and medium scale models, which included using various types of open-source algorithms. One of the key benefits of SageMaker is that various types of algorithms can be brought into SageMaker and deployed using a bring your own container (BYOC) technique.
The system used advanced analytics and mostly classic machine learning algorithms to identify patterns and anomalies in claims data that may indicate fraudulent activity. If you aren’t aware already, let’s introduce the concept of ETL. We primarily used ETL services offered by AWS. Redshift, S3, and so on.
This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an MLengineer. Jeff Magnusson has a pretty famous post about engineers shouldn’t write ETL. Stefan: Yeah.
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