AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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The MLOps Blog

MARCH 15, 2023

For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem. Mitigating the problem of data drift Source One among our other concerns was data drift, which usually occurs when the data used in production slowly changes in some aspects over time from the data used to train the model.

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ETL Data Drift Machine Learning ML 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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Machine Learning Data Drift Data Science ML Engineer 52

The MLOps Blog

MARCH 28, 2023

.” — Paweł Pęczek, Machine Learning Engineer at Brainly The goal of working at this level is to ensure that the model is of the highest quality and to eliminate any problems that could arise early during development. They also need to monitor and see changes in the data distribution ( data drift, concept drift , etc.)

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Machine Learning Automation Data Scientist ML 59

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” That is definitely a problem. That’s where you start to see data drift. Robert, you can go first.

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Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” That is definitely a problem. That’s where you start to see data drift. Robert, you can go first.

Large Language Models 52

Large Language Models Metadata AI AI 52