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Basil Faruqui, BMC: Why DataOps needs orchestration to make it work

AI News

The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. It’s all data driven,” Faruqui explains. And everybody agrees that in production, this should be automated.”

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Foundational models at the edge

IBM Journey to AI blog

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps.

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Using Agents for Amazon Bedrock to interactively generate infrastructure as code

AWS Machine Learning Blog

Select the KB and in the Data source section, choose Sync to begin data ingestion. When data ingestion completes, a green success banner appears if it is successful. Use the managed vector store to allow Amazon Bedrock to create and manage the vector store for you in Amazon OpenSearch Service.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

The model will be approved by designated data scientists to deploy the model for use in production. For production environments, data ingestion and trigger mechanisms are managed via a primary Airflow orchestration. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies. Mateusz Zaremba is a DevOps Architect at AWS Professional Services. Amazon Transcribe’s new ASR foundation model supports 100+ language variants.

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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market.

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Introducing the Amazon Comprehend flywheel for MLOps

AWS Machine Learning Blog

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. An Amazon Comprehend flywheel automates this ML process, from data ingestion to deploying the model in production.