Definition DevOps ML Engineer 
AWS Machine Learning Blog
SEPTEMBER 4, 2024
Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. In this post, we introduce an example to help DevOps engineers manage the entire ML lifecycle—including training and inference—using the same toolkit.
O'Reilly Media
OCTOBER 19, 2021
This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?
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The MLOps Blog
JANUARY 23, 2023
Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Lived through the DevOps revolution. Came to ML from software. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.
AWS Machine Learning Blog
DECEMBER 19, 2023
Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Machine learning operations (MLOps) applies DevOps principles to ML systems.
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.
AWS Machine Learning Blog
OCTOBER 22, 2024
Data scientists and machine learning (ML) engineers use pipelines for tasks such as continuous fine-tuning of large language models (LLMs) and scheduled notebook job workflows. Download the pipeline definition as a JSON file to your local environment by choosing Export at the bottom of the visual editor.
AWS Machine Learning Blog
AUGUST 22, 2024
As you move from pilot and test phases to deploying generative AI models at scale, you will need to apply DevOps practices to ML workloads. In the notebook, we already added the @step decorator at the beginning of each function definition in the cell where the function was defined, as shown in the following code.
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