In this article, I will discuss about various aspects of building an AI dashboard using Metabase, and deploying it in AWS. I recently came across an interesting open-sourced BI tool called Metabase. As a basic-level user of Qlik, I was quickly impressed by the simplicity of Metabase.
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Easy deployment in cloud services such as AWS, Azure, Heroku or Google cloud.Ability to apply various filtering features during the creation of dashboards.Ability to create various kinds of plots which are suitable to present data of different forms.Ability to easily connect to various databases.Although it is subject to the problem (or rather the business requirement) at hand, I often weigh the BI tools based on the following features: To this end, BI tools often come up with features such as capabilities to create various plots and dashboards, easy integration with various database systems, etc. Deploying the application either on-premise or in cloud.Creating dashboards which provide various business insights in terms of collection of a large number of plots (or KPIs).Creating plots which answer various business questions.
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Bringing data from various sources (e.g., from AI models via databases (DBs)).The typical work-flow of BI tools, at least in the context of AI & ML, includes the followings: In this respect, Business Intelligence (BI) tools play a significant role in bringing various AI & ML insights into the hands of business experts.
#Metabase open source code
Note that AGPL licensed code is very different from 'Open Source which means I can do whatever I want with it' code, which is more what MIT or other more permissive licenses allow. You can use it as you desire, so long as you abide by the APGL. Effective presentation and interactive visualisation of outputs from various machine learning models are vital aspects of bringing Artificial Intelligence (AI) and Machine Learning (ML) close to actual businesses. The Metabase application is open source (under the APGL) and free.