AWS Machine Learning Blog

MARCH 1, 2023

Continuous ML model retraining is one method to overcome this challenge by relearning from the most recent data. This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process. But there is still an engineering challenge.

Automation 99

Automation ETL Data Drift ML 99

AWS Machine Learning Blog

FEBRUARY 21, 2025

To promote the success of this migration, we collaborated with the AWS team to create automated and intelligent digital experiences that demonstrated Rockets understanding of its clients and kept them connected. With just one part-time ML engineer for support, our average issue backlog with the vendor is practically non-existent.

Data Science 70

Data Science Data Scientist Data Ingestion DevOps 70

The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , 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.

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ETL ML Machine Learning Data Scientist 59

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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ML Machine Learning Data Scientist ETL 123

AWS Machine Learning Blog

JANUARY 5, 2024

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost.

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Data Science Data Drift DevOps Auto-complete 131

AWS Machine Learning Blog

AUGUST 4, 2023

It eliminates tedious, costly, and error-prone ETL (extract, transform, and load) jobs. For example, you can use automated workflows that can adapt in an instant based on new data. We also provided an automated solution to deploy the SageMaker endpoints as an API using a SageMaker Projects template for Salesforce.

Data Scientist 88

Data Scientist ML ETL Data Platform 88

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Generative AI Prompt Engineering Prompt Engineer ML 124