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5G network rollout using DevOps: Myth or reality?

IBM Journey to AI blog

This requires a careful, segregated network deployment process into various “functional layers” of DevOps functionality that, when executed in the correct order, provides a complete automated deployment that aligns closely with the IT DevOps capabilities. It also takes care of the major upgrades on the network function.

DevOps 242
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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Choose the right technology and tools Select tools that support data cataloging, lineage tracking, metadata management and data quality monitoring, helping to ensure integration with the organization’s existing data management infrastructure for a seamless transition.

Metadata 189
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OpenTelemetry vs. Prometheus: You can’t fix what you can’t see

IBM Journey to AI blog

OpenTelemetry and Prometheus enable the collection and transformation of metrics, which allows DevOps and IT teams to generate and act on performance insights. Benefits of OpenTelemetry The OpenTelemetry protocol (OTLP) simplifies observability by collecting telemetry data, like metrics, logs and traces, without changing code or metadata.

DevOps 263
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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

Lived through the DevOps revolution. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. There will be only one type of ML metadata store (model-first), not three. Came to ML from software.

DevOps 59
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Autonomous Agents with AgentOps: Observability, Traceability, and Beyond for your AI Application

Unite.AI

This is where AgentOps comes in; a concept modeled after DevOps and MLOps but tailored for managing the lifecycle of FM-based agents. The Taxonomy of Traceable Artifacts The paper introduces a systematic taxonomy of artifacts that underpin AgentOps observability: Agent Creation Artifacts: Metadata about roles, goals, and constraints.

LLM 182
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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. They often work with DevOps engineers to operate those pipelines. curl for transmitting data with URLs.

DevOps 94
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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

Flipboard

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , Amazon SageMaker , AWS DevOps services, and a data lake. Data engineers contribute to the data lineage process by providing the necessary information and metadata about the data transformations they perform.

ML 132