Operations | Monitoring | ITSM | DevOps | Cloud

Chaos Engineering has proven itself to be incredibly effective at tracking down failure modes, remediating reliability issues, and preventing risks before they happen. Unfortunately, it can also come with a steep adoption curve. In order to get the most out of Fault Injection testing, a practitioner needs to have a deep knowledge of the service, its expected behavior, and the code behind it. Ultimately, the rewards are worth the time.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

AI coding assistants are already transforming how developers work, helping them write code faster, answer tough questions, and automate repetitive tasks. It’s exciting, it’s powerful… and it’s just the beginning. Cortex MCP connects your AI assistant directly to your live service data, ownership, and organizational standards so it can give accurate, context-rich answers right in your IDE.

Our CLI team has been cooking up some awesome new AI functionality that we think you will love. The latest release includes several new commands such as changelog, commit, pr, and a command to check the remaining tokens on your subscription. They are all nested under a new top level command, gk ai.

Software quality assurance has changed dramatically over the past few years. Today, the velocity of software development demands more than traditional staging and shared QA environments. Releases are expected to be faster, integration cycles shorter, and quality standards higher. These pressures have inspired a growing interest in preview environments—ephemeral, production-like spaces spun up on demand for testing code changes in isolation.