AWS Machine Learning Blog

AUGUST 4, 2023

It eliminates tedious, costly, and error-prone ETL (extract, transform, and load) jobs. SageMaker Projects provides a straightforward way to set up and standardize the development environment for data scientists and ML engineers to build and deploy ML models on SageMaker.

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TheSequence

MAY 21, 2023

📢 Event: apply(risk), the ML Engineering Community Conference for Building Risk & Fraud Detection Systems Want to connect with the ML engineering community and learn best practices from ML practitioners at Affirm, Remitly, Block, Tide, and more, on how to build risk and fraud detection systems?

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The MLOps Blog

MARCH 15, 2023

.” Hence the very first thing to do is to make sure that the data being used is of high quality and that any errors or anomalies are detected and corrected before proceeding with ETL and data sourcing. If you aren’t aware already, let’s introduce the concept of ETL. We primarily used ETL services offered by AWS.

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AWS Machine Learning Blog

JANUARY 5, 2024

With experience of leading AWS AI/ML solutions across industries, Bhajandeep has enabled clients to maximize the value of AWS AI/ML services through his expertise and leadership. Ajay Vishwakarma is an ML engineer for the AWS wing of Wipro’s AI solution practice.

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The MLOps Blog

JANUARY 26, 2024

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline?

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Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability.

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Machine Learning ML Data Drift Data Quality 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability.

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Machine Learning ML Data Drift Data Quality 52

Mlearning.ai

FEBRUARY 9, 2023

While dealing with larger quantities of data, you will likely be working with Data Engineers to create ETL (extract, transform, load) pipelines to get data from new sources. Data Science is an umbrella role with common roles such as Data Analytics, research, ML model building, ML Ops, and ML engineering underneath.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. While often ignored by data scientists, I believe mastering ETL is core and critical to guarantee the success of any machine learning project.

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The MLOps Blog

MARCH 28, 2023

Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data. It’s a healthy situation when data scientists and ML engineers, in particular, are responsible for delivering tests for the functionalities of their projects.

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The MLOps Blog

AUGUST 3, 2023

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 ML engineer. Jeff Magnusson has a pretty famous post about engineers shouldn’t write ETL. Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 52

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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Flipboard

NOVEMBER 27, 2024

He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises. Gi Kim is a Data & ML Engineer with the AWS Professional Services team, helping customers build data analytics solutions and AI/ML applications.

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