Data Platform DevOps Metadata 
DataRobot Blog
MARCH 16, 2023
An AI platform that works well with a broad enterprise ecosystem: A platform that seamlessly integrates with the substantial investments businesses have already made in infrastructure, practitioner tools, data platforms and business applications.
AWS Machine Learning Blog
MARCH 5, 2025
The examples focus on questions on chunk-wise business knowledge while ignoring irrelevant metadata that might be contained in a chunk. He has touched on most aspects of these projects, from infrastructure and DevOps to software development and AI/ML. Rahul Jani is a Data Architect with AWS Professional Service.
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AWS Machine Learning Blog
OCTOBER 20, 2023
The architecture maps the different capabilities of the ML platform to AWS accounts. The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake.
The MLOps Blog
AUGUST 3, 2023
Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah. Thanks for having me.
The MLOps Blog
OCTOBER 3, 2023
I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. You shifted straight from data science, if I understand correctly.
The MLOps Blog
MARCH 21, 2023
To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. You need to build your ML platform with experimentation and general workflow reproducibility in mind.
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