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Lambda Pinball" is a related anti-pattern that can occur in serverless architectures when functions (e.g., AWS Lambda, Azure Functions) excessively invoke each other in fragmented chains, leading to latency, debugging and testing challenges, and reduced observability. [4]
AWS launches AWS Lambda, its Functions as a Service (FaaS) tool. With Lambda, AWS customers can define and upload functions with specific triggers and execution code. AWS takes care of executing the function on the trigger occurring, and the AWS customer does not have to provision or manage the compute resources.
AWS Lambda layer is a ZIP archive containing libraries, frameworks or custom code that can be added to AWS Lambda functions. [9] As of December 2024, AWS Lambda layers have significant limitations: [10] [11] No semantic versioning support. Incompatibility with major security scanning tools. Contribution to Lambda's 250MB size limit. Impeded ...
Amazon SageMaker AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. [1] It can be used to deploy ML models on embedded systems and edge-devices. [2] [3] The platform was launched in November 2017. [4]
An API can also be related to a software framework: a framework can be based on several libraries implementing several APIs, but unlike the normal use of an API, the access to the behavior built into the framework is mediated by extending its content with new classes plugged into the framework itself.
Serverless computing is "a cloud service category in which the customer can use different cloud capability types without the customer having to provision, deploy and manage either hardware or software resources, other than providing customer application code or providing customer data.
Foundation models are built by optimizing a training objective(s), which is a mathematical function that determines how model parameters are updated based on model predictions on training data. [34] Language models are often trained with a next-tokens prediction objective, which refers to the extent at which the model is able to predict the ...
Continuing the previous example, consider two processes, A and B, running the same program. Suppose SimpleDB services requests for data, as described in step 1, from both A and B. A and B see the same value. Let's say that the current value of the counter is 0. Because of steps 2 and 3, A will try to store 1.