Curated articles, resources, tips and trends from the DevOps World.
When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions.
To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data.
Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis.
Gathering insights from data is a more effective process if that data isn’t fragmented across multiple systems and data stores, whether on premises or in the cloud.
As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle.
Back in 1980, at my second professional programming job, I was working on a project that analyzed driver’s license data from a bunch of US states. At that time data of that type was generally stored in fixed-length records, with values carefully (or not) encoded into each field.
PostgreSQL has become the preferred open-source relational database for many enterprises and start-ups with its extensible design for developers.
You use map apps every day to find your favorite restaurant or travel the fastest route using geospatial data.
Today, I’m excited to announce a new, redesigned user interface (UI) for Amazon SageMaker Studio. SageMaker Studio provides a single, web-based visual interface where you can perform all machine learning (ML) development steps with a comprehensive set of ML tools.
Amazon DocumentDB (with MongoDB compatibility) is a scalable, highly durable, and fully managed database service for operating mission-critical JSON workloads.
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