AWS Machine Learning Blog

FEBRUARY 21, 2025

Steep learning curve for data scientists: Many of Rockets data scientists did not have experience with Spark, which had a more nuanced programming model compared to other popular ML solutions like scikit-learn. Despite the support of our internal DevOps team, our issue backlog with the vendor was an unenviable 200+.

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AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Related post MLOps Is an Extension of DevOps. 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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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 126

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. The AI/ML teams are in the services department under infrastructure teams but related to AI, and a few AI teams are working on ML-based solutions that clients can consume. On top of the teams, they also have departments.

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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

How to Learn Machine Learning

APRIL 10, 2025

Seamless AWS Integration Works effortlessly with AWS S3 (data storage), AWS Lambda (serverless computing), and AWS Glue (ETL). Challenges of Custom ML Initial deployment costs are high because DevOps teams and ML engineers must be hired while infrastructure expenditure is necessary.

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ML DevOps Auto-complete Machine Learning 59