Category: Amazon QuickSight

This post was co-written by Michael Wirig, Software Engineering Manager at Grōv Technologies. A substantial percentage of the world’s habitable land is used for livestock farming for dairy and meat production. The dairy industry has leveraged technology to gain insights that have led to drastic improvements and are continuing to accelerate. A gallon of milk […]

Customers who maintain manufacturing facilities often find it challenging to ingest, centralize, and visualize IoT data that is emitted in flat-file format from their factory equipment. While modern IoT-enabled industrial devices can communicate over standard protocols like MQTT, there are still some legacy devices that generate useful data but are only capable of writing it […]

In an era where more and more data is generated, it becomes critical for businesses to derive value from it. With the help of supervised learning, it is possible to generate models to automatically make predictions or decisions by leveraging historical data. For example, image recognition for self-driving cars, predicting anomalies on X-rays, fraud detection […]

Introduction Too often, architects fall into the trap of thinking the architecture of an application is restricted to just the runtime part of the architecture. By doing this we focus on only a single customer (such as the application’s users and how they interact with the system) and we forget about other important customers like […]

As per International Data Corporation (IDC), worldwide spending on digital transformation will reach $2.3 trillion in 2023. As organizations adopt digital transformation, training becomes an important aspect of this journey. Whether these are internal trainings to upskill existing workforce or a packaged content for commercial use, these trainings need to be efficient and cost effective. […]

Leveraging Analytics and Machine Learning Tools for Readmissions Prediction This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team. In Part 1, we looked at various options to ingest and store sensitive healthcare data using AWS. The post described our shared responsibility model and provided a […]