Remove DevOps Remove Metadata Remove Software Development
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OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

IBM Journey to AI blog

Monitoring and optimizing application performance is important for software developers and enterprises at large. OpenTelemetry and Prometheus enable the collection and transformation of metrics, which allows DevOps and IT teams to generate and act on performance insights. What is OpenTelemetry?

DevOps 243
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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

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.

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Fine tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators

AWS Machine Learning Blog

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. 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.

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Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning Blog

The output of a SageMaker Ground Truth labeling job is a file in JSON-lines format containing the labels and additional metadata. With a passion for automation, Joerg has worked as a software developer, DevOps engineer, and Site Reliability Engineer in his pre-AWS life.

DevOps 132
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The Role of DevSecOps in Ensuring Data Privacy and Security in Data Science Projects

ODSC - Open Data Science

This shift in thinking has led us to DevSecOps , a novel methodology that integrates security into the software development/ MLOps process. DevSecOps includes all the characteristics of DevOps, such as faster deployment, automated pipelines for build and deployment, extensive testing, etc.,

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Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions

AWS Machine Learning Blog

Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance. It enables teams to collaborate on software development projects, track changes, and manage code repositories. Building a robust MLOps pipeline demands cross-functional collaboration.

ML 132
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MLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMaker

AWS Machine Learning Blog

SageMaker deployment guardrails Guardrails are an essential part of software development. Similar to traditional CI/CD systems, we want to automate software tests, integration testing, and production deployments. All of the metadata for these experiments can be tracked using Amazon SageMaker Experiments during development.

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