The primary service you would use for periodic, automated execution of an ML workflow within the SageMaker ecosystem is SageMaker Pipelines or, for simpler scheduling, a combination of Amazon EventBridge (the scheduler) and SageMaker Processing Jobs or Training Jobs (the executor). Here is a breakdown of the three best ways to achieve this, from simplest...Read More
Amazon SageMaker is a powerful, fully managed service provided by Amazon Web Services (AWS) that is designed to help data scientists and developers quickly and easily build, train, and deploy machine learning (ML) models at scale. It essentially simplifies and automates many of the labor-intensive tasks throughout the entire ML lifecycle, from data preparation all...Read More
Redis is an in-memory data structure store often used as a database, cache, and message broker. It’s renowned for its speed and flexibility. Core Concepts In-memory: Data is stored in RAM for incredibly fast access. 1. About – Redis redis.io Key-value store: Data is organized as key-value pairs. 1. What is a...Read More
