Operations | Monitoring | ITSM | DevOps | Cloud

AWS recently introduced a new Lambda Telemetry API giving users the ability to collect logs, metrics, and traces for analysis in AWS services like Cloudwatch or a third-party observability platform like Coralogix. It allows for a simplified and holistic collection of observability data by providing Lambda extensions access to additional events and information related to the Lambda platform.

The process of delivering mobile apps utilizing cloud technology is known as mobile cloud computing (MCC). Complex mobile apps today carry out activities including authentication, location-aware features and providing users with customized communication and content. As long as your device is online, mobile cloud computing enables you to store and access data anywhere. This makes it possible for data to be sent without difficulty anytime required.

You can now use Checkly to monitor API endpoints secured with TLS Client Certificates. This post dives into why and how you would use client certificates and mTLS (Mutual TLS) in your API infrastructure. Let's go!

I have been building a feature on elmah.io lately that picks up validation results from an external API. Before serverless was a thing, I would probably have done this using a scheduled task or Windows Service using Hangfire or similar. But after having migrated everything to serverless Azure Functions, I wanted a good solution running similarly. Azure Durable Functions turned out as the perfect companion and in this post, I'll show you a possible way to implement polling of an asynchronous API.

On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by Huawei. Charmed Kubeflow is an end-to-end MLOps platform with optimised complex model training capabilities designed for use with Kubernetes.

Looking at the report that Gartner did in 2022 regarding top technology trends, AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning.

Maintain high uptime and performance for your APIs without any overheads using Google Cloud’s API monitoring tools.