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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. Amid this infrastructure Control-M, in the words of Hershey’s analyst Todd Lightner, ‘literally runs our business.’

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Azure service cloud summarized: Part I

Mlearning.ai

But, it does not give you all the information about the different functionalities and services, like Data Factory/Linked Services/Analytics Synapse(how to combine and manage databases, ETL), Cognitive Services/Form Recognizer/ (how to do image, text, audio processing), IoT, Deployment, GitHub Actions (running Azure scripts from GitHub).

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Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart

AWS Machine Learning Blog

The embeddings are captured in Amazon Simple Storage Service (Amazon S3) via Amazon Kinesis Data Firehose , and we run a combination of AWS Glue extract, transform, and load (ETL) jobs and Jupyter notebooks to perform the embedding analysis. Set the parameters for the ETL job as follows and run the job: Set --job_type to BASELINE.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Data science and DevOps teams may face challenges managing these isolated tool stacks and systems. AWS also helps data science and DevOps teams to collaborate and streamlines the overall model lifecycle process. This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Software Engineering Patterns for Machine Learning

The MLOps Blog

From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc. Related post MLOps Is an Extension of DevOps. Explore how these principles can elevate the quality of your ETL work.

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Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly

The MLOps Blog

The DevOps and Automation Ops departments are under the infrastructure team. The infrastructure team focuses on technology and delivers tools that other teams will adapt and use to work on their main deliverables. On top of the teams, they also have departments.