Remove dev-central
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Prod environment – Where the ML pipelines from dev are promoted to as a first step, and scheduled and monitored over time. Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments.

Policies 102
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How Yara is using MLOps features of Amazon SageMaker to scale energy optimization across their ammonia plants

AWS Machine Learning

To facilitate faster development in a multi-team environment, Yara chose to use AWS Landing Zo ne and Organizations to centrally create, manage, and govern different AWS accounts. For example, Yara has a central deployment account, and uses workload accounts to host business applications. CI/CD flow.

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What's changed in the next generation of Khoros Communities?

Lithium

Next, the new version of Khoros Communities has a built-in centralized content management dashboard, where users can perform all tasks related to content moderation , including accessing drafts and reviewing spam and abusive content. Unsplash image libraries have been integrated for blog cover images and page template sections.

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Intelligently search Drupal content using Amazon Kendra

AWS Machine Learning

Amazon Kendra helps you easily aggregate content from a variety of content repositories into a centralized index that lets you quickly search all your enterprise data and find the most accurate answer. His areas of depth span Machine Learning, app/mobile dev, event-driven architecture, and IoT/edge computing.

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Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access. This framework allows different teams to build and deploy ML models independently while providing central governance.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Each business unit has each own set of development (automated model training and building), preproduction (automatic testing), and production (model deployment and serving) accounts to productionize ML use cases, which retrieve data from a centralized or decentralized data lake or data mesh, respectively.

Training 105
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Tech Highlights from InFocus 2020

Circular Edge

– User Spec Repository Tables in Central Objects Datasource in place of local specs on FAT client for development. This would also allow portable development where you can start work on one dev client and pick it up on another (if it’s not business function changes).