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MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Feature Engineering.

DevOps 140
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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

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. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Ok, let me explain.

DevOps 59
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The Hybrid Cloud Forecast: Part 2

IBM Journey to AI blog

One of the few pre-scripted questions I ask in most of the episodes is about the guest’s definition of “hybrid cloud.” Rob High explained the increasing importance of running applications in non-traditional places and on non-traditional devices, which we also often call “on the edge.”

DevOps 189
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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

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.

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Best practices for hybrid cloud banking applications secure and compliant deployment across IBM Cloud and Satellite

IBM Journey to AI blog

To deploy applications onto these varying environments, we have developed a set of robust DevSecOps toolchains to build applications, deploy them to a Satellite location in a secure and consistent manner and monitor the environment using the best DevOps practices. DevSecOps workflows focus on a frequent and reliable software delivery process.

DevOps 290
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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning Blog

Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. This triggers the creation of the model deployment pipeline for that ML model.

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Foundational models at the edge

IBM Journey to AI blog

IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows. IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.