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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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Splunk Tutorial For Beginners: It’s Application & Features

Pickl AI

It initiates the collection, indexing, and analysis of machine-generated data in real-time. It helps harness the power of big data and turn it into actionable intelligence. Moreover, it allows users to ingest data from different sources. Additionally, Splunk can process and index massive volumes of data.

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Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.