The MLOps Blog

APRIL 5, 2023

Source Model packaging is a process that involves packaging model artifacts, dependencies, configuration files, and metadata into a single format for effortless distribution, installation, and reuse. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.

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Unite.AI

NOVEMBER 20, 2024

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.

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The MLOps Blog

OCTOBER 3, 2023

For me, it was a little bit of a longer journey because I kind of had data engineering and cloud engineering and DevOps engineering in between. There’s no component that stores metadata about this feature store? Mikiko Bazeley: In the case of the literal feature store, all it does is store features and metadata. We offer that.

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The MLOps Blog

MAY 9, 2023

2 Analyzes the model’s interpretability/explainability to help you understand the quality and biases of the model or models you plan to deploy. Is it a black-box model, or can the decisions be explained? For example, you could use metadata to determine which version of your model was in production at a given time.

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AWS Machine Learning Blog

JANUARY 13, 2023

TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. Increase transparency and collaboration by creating a centralized view of all models across TR alongside metadata and health metrics. Data service. Model deployment.

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The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles.

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AWS Machine Learning Blog

JANUARY 9, 2025

DSX provides unmatched prevention and explainability by using a powerful combination of deep learning-based DSX Brain and generative AI DSX Companion to protect systems from known and unknown malware and ransomware in real-time. This situation hampers proactive threat hunting and exacerbates team burnout.

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