Fig 4 - uploaded by Iman Shakeel
Content may be subject to copyright.
Context in source publication
Context 1
... the state machines can be activated synchronously or asynchronously. The Step Functions console, AWS SDKs, AWS Command Line Interface (CLI), or Step Functions API can all be used to invoke the state machines. JSON-formatted input is sent to step functions, which are subsequently passed on to the various states of the state machine. As depicted in Fig. 4, we can set up several filter types to change data in each state both before and ahead of task processing. Information about the customer who placed the order, the item they ordered, and the order details are all included in the input data. Check inventory is the first state that receives this input data. The state machine executes the ...
Similar publications
Background The adoption of technology in health care settings is often touted as an opportunity to improve patient safety. While some adverse events can be reduced by health information technologies, technology has also been implicated in or attributed to safety events. To date, most studies on this topic have focused on acute care settings.
Object...
Citations
... Lambda lets you run code without provisioning or managing servers, which can be particularly helpful for individual tasks within an MLOps pipeline. In contrast, Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows [7]. ...
Machine Learning Operations (MLOps), a discipline at the intersection of data science and DevOps, addresses the demand for speed and scale in deploying machine learning models. However, building a comprehensive MLOps pipeline that is both effective and scalable presents numerous challenges. This paper proposes a systematic framework to build end-to-end MLOps tools for creating scalable machine learning systems using Amazon Web Services (AWS). My framework combines a variety of AWS services to automate every step of the machine learning workflow, from data storage and preparation to model development, deployment, and monitoring. This cohesive approach minimizes manual intervention, improves operational efficiency, and enables a high degree of scalability. I illustrate the practical application of my framework with a case study involving a mid-sized e-commerce company, which yields enhanced customer experience and improved business metrics. The proposed solution holds significant potential to streamline MLOps workflows across diverse industries, allowing them to harness the full potential of machine learning at scale.