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Streamlining ETL data processing at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository. session.Session().region_name

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How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Deployment times stretched for months and required a team of three system engineers and four ML engineers to keep everything running smoothly.

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How to Build ETL Data Pipeline in ML

The MLOps Blog

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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Software Engineering Patterns for Machine Learning

The MLOps Blog

This situation is not different in the ML world. Data Scientists and ML Engineers 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.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In addition to the challenge of defining the features for the ML model, it’s critical to automate the feature generation process so that we can get ML features from the raw data for ML inference and model retraining.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?

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Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning Blog

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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