AWS Machine Learning Blog

FEBRUARY 21, 2025

The concept of a compound AI system enables data scientists and ML engineers to design sophisticated generative AI systems consisting of multiple models and components. Yunfei has a PhD in Electronic and Electrical Engineering. His area of research is all things natural language (like NLP, NLU, and NLG).

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AWS Machine Learning Blog

NOVEMBER 30, 2023

In this example, Code Editor can be used by an ML engineering team who needs advanced IDE features to debug their code and deploy the endpoint. He has worked on projects in different domains, including MLOps, computer vision, and NLP, involving a broad set of AWS services. You can find the sample code in this GitHub repo.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon Comprehend is a natural language processing (NLP) service that uses ML to uncover insights and relationships in unstructured data, with no managing infrastructure or ML experience required. Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle.

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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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The MLOps Blog

OCTOBER 3, 2023

I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. I see so many of these job seekers, especially on the MLOps side or the ML engineer side. It’s two things.

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The MLOps Blog

JULY 18, 2023

You have a bit of education in music composition, math, and science before you get more into the software engineering side of things. But you have started out in software design engineering, is that correct? How do you ensure data quality when building NLP products? Jason: Yeah, that’s right. Stephen: Great.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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Unite.AI

DECEMBER 15, 2023

My role at Prolific is split between being an advisor regarding AI use cases and opportunities, and being a more hands-on ML Engineer. I started my career in Software Engineering and have gradually transitioned to Machine Learning. I’ve spent most of the last 5 years focused on NLP use cases and problems.

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Snorkel AI

MAY 24, 2023

He has over 30 publications and more than 20 patents in machine learning and NLP. Ali Arsanjani , director of cloud partner engineering at Google Cloud, will join the conference for a panel about the power and responsibility of adopting AI. He was previously a senior leader at AWS, and the CTO of Analytics & ML at IBM.

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Snorkel AI

MAY 24, 2023

He has over 30 publications and more than 20 patents in machine learning and NLP. Ali Arsanjani , director of cloud partner engineering at Google Cloud, will join the conference for a panel about the power and responsibility of adopting AI. He was previously a senior leader at AWS, and the CTO of Analytics & ML at IBM.

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AWS Machine Learning Blog

OCTOBER 17, 2024

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. He has worked on a wide range of projects spanning NLP, computer vision, and generative AI. He also has experience with building end-to-end MLOps pipelines to productionize analytical models.

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ODSC - Open Data Science

MARCH 11, 2025

About the Author of Adaptive RAG Systems: David vonThenen David is a Senior AI/ML Engineer at DigitalOcean, where hes dedicated to empowering developers to build, scale, and deploy AI/ML models in production.

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