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Building your First Power BI Report from Scratch

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. What is equally important here is the ability to communicate the data and insights from your predictive models through reports and dashboards. PowerBI is used for Business intelligence. And […].

Reporting 389
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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. We are still in the early days for tools supporting teams developing machine learning models. Model governance.

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Top 10 Power BI Interview Questions in 2022

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Power BI interviews will provide insights from a variety of data by modelling data and telling stories from data visualizations using reports and dashboards. Source: [link] […].

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In the following section, two use cases demonstrate how the data mesh is established with Amazon DataZone to better facilitate machine learning for an IoT-based digital twin and BI dashboards and reporting using Tableau. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 100
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Marketing Dashboard: Secretes To Adapt To Fast-Changing Market

FineReport

One of the most difficult problems faced by marketers is how to analyze all the information, which comes from customer surveys, from complex computer models, and from various channels. Marketing dashboard can do you a favor. What is a marketing dashboard? Use of Reports and Dashboard. Dashboard Interface of FineReport.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk. Model risk management. Image by Ben Lorica.

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The Journey to DataOps Success: Key Takeaways from Transformation Trailblazers

DataKitchen

Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. 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.