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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Why: Data Makes It Different. If you ask an engineer to show how they operate the application in production, they will likely show containers and operational dashboards—not unlike any other software service. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?

IT 364
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Data transformation takes flight at Atlanta’s Hartsfield-Jackson airport

CIO Business Intelligence

At Atlanta’s Hartsfield-Jackson International Airport, an IT pilot has led to a wholesale data journey destined to transform operations at the world’s busiest airport, fueled by machine learning and generative AI. Applying AI to elevate ROI Pruitt and Databricks recently finished a pilot test with Microsoft called Smart Flow.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Their terminal operations rely heavily on seamless data flows and the management of vast volumes of data. Recently, EUROGATE has developed a digital twin for its container terminal Hamburg (CTH), generating millions of data points every second from Internet of Things (IoT)devices attached to its container handling equipment (CHE).

IoT 111
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Amazon OpenSearch Service launches flow builder to empower rapid AI search innovation

AWS Big Data

This middleware consists of custom code that runs data flows to stitch data transformations, search queries, and AI enrichments in varying combinations tailored to use cases, datasets, and requirements. Ingest flows are created to enrich data as its added to an index. Flows are a pipeline of processor resources.

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Streamline AI-driven analytics with governance: Integrating Tableau with Amazon DataZone

AWS Big Data

Amazon DataZone is a data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across AWS, on premises, and from third-party sources. You can now view your project’s subscribed data directly within Tableau and build dashboards.

Analytics 119
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The Journey to DataOps Success: Key Takeaways from Transformation Trailblazers

DataKitchen

Similarly, Workiva was driven to DataOps due to an increased need for analytics agility to meet a range of organizational needs, such as real-time dashboard updates or ML model training and monitoring. There are a limited number of folks on the data team that can manage all of these things. Others have difficulty collaborating.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

What Is Data Quality Management (DQM)? Data quality management is a set of practices that aim at maintaining a high quality of information. It goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data.