DevOps Metadata Software Development 
The MLOps Blog
JANUARY 23, 2023
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.
IBM Journey to AI blog
MARCH 29, 2024
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?
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AWS Machine Learning Blog
AUGUST 22, 2024
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.
ODSC - Open Data Science
APRIL 17, 2023
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.,
AWS Machine Learning Blog
OCTOBER 2, 2023
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.
The MLOps Blog
APRIL 17, 2023
Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.
Viso.ai
MARCH 29, 2024
MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework.
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