Remove DevOps Remove Information Remove Metadata
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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. They should also have access to relevant information about how data is collected, stored and used.

Metadata 189
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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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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 101
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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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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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Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning Blog

When defining your tagging strategy, you need to determine the right tags that will gather all the necessary information in your environment. Technical tags – These provide metadata about resources. The AWS reserved prefix aws: tags provide additional metadata tracked by AWS.

ML 96
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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning Blog

These indexes enable efficient searching and retrieval of part data and vehicle information, providing quick and accurate results. The agents also automatically call APIs to perform actions and access knowledge bases to provide additional information. The embeddings are stored in the Amazon OpenSearch Service owner manuals index.

DevOps 126