AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to move data between data stores. It was first introduced in 2017 as a way to simplify the process of extracting data from various sources, transforming it into the desired format, and loading it into a data warehouse or other data store for analysis and reporting.
As fans of the hit Netflix series “Stranger Things,” we’re always on the lookout for ways to bring the Upside Down into our everyday lives. And what better way to do that than with AWS Batch, the cloud computing service that allows you to run batch processing jobs at any scale? In this blog post, we’ll explore how AWS Batch can help you demogorgon-proof your data processing, making it faster and more efficient than ever before. So grab your Eggo waffles and get ready to enter the world of AWS Batch!
As the Britpop band Oasis sang, “Why hope for the turning, if everything you know is wrong?”. The same can be said for traditional approaches to building machine learning models. Enter Google Cloud Platform and BigQuery, the tools that are shaking up the world of machine learning and helping businesses and researchers alike to take their models to new heights.
Welcome to the world of MLOps on AWS, where the rhythms of machine learning and cloud operations merge to create a futuristic symphony of data-driven decision making. Imagine yourself cruising down the information superhighway in a DeLorean, jamming out to the sounds of “Take On Me” by A-ha. As you enter the cloud computing landscape, you’re met with the powerful and flexible tools of AWS, ready to help you train and deploy machine learning models at scale. With MLOps on AWS, you can optimize your model performance and manage your infrastructure with ease, all while feeling like a total “Material Girl” in the world of AI. So buckle up and get ready to rock out with MLOps on AWS – the future of machine learning is here and it’s totally rad!
Machine Learning pipelines give rise to challenges for the teams which build and maintain them. Changes to data over time can give rise to unexpected outcomes, which leads to bugs in places where you don’t find them in other software. Understanding your data and how it may change over time makes reasoning about machine learning pipelines easier. ML teams need additional tools to help keep their models up to date and predictions relevant, TFX is one such tool.