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At AWS re:Invent today, Amazon announced updates to its generative AI assistant for software developers, Amazon Q Developer.  There are three new agents that automate unit testing, documentation, and code reviews. Developers can make it generate a test by typing “/test” in the chat window or highlighting a specific block of code to test. It utilizes its overall knowledge of the project to generate the necessary tests for the code.  Similarly, developers can now type “/doc” in the chat window to get the assistant to automatically generate documentation and update README files. Developers can also ask questions about how their code works or ask it to improve existing documentation.  And finally, typing “/review” will make Amazon Q flag suspicious code patterns, identify open source package risks, and assess the impact of changes. They can also use “/q review” in GitLab Duo to have Amazon Q review their merge requests.  RELATED: AI agents are transforming the softwa...

  The AI platform Clarifai is attempting to make it easier for companies to leverage their existing cloud and hardware investments for AI. Clarifai already offers tools to assist throughout the whole AI lifecycle, including data labeling, training, evaluation, workflows, and feedback.  Now in public preview, Compute Orchestration provides companies with a single control plane for governing access to AI resources, monitoring performance, and managing costs. Customers can deploy any model using any hardware vendor in any cloud, on-premises, air-gapped, or SaaS environments, which is a major expansion of the deployment options Clarifai offers. According to Clarifai, Compute Orchestration uses clusters and nodepools to organize and manage compute resources. A compute cluster is the “overarching computational environment where models are executed,” while nodepools are a set of dedicated nodes within a cluster that share similar configurations. “Cluster configuration lets yo...

At AWS re:Invent, Amazon announced a number of new capabilities related to observability to help developers gain more visibility into their applications.  The company is introducing enhanced observability capabilities for container workloads running on Amazon Elastic Container Service (Amazon ECS). According to AWS, this announcement ties in with Container Insights, which was a capability introduced last year to improve container observability.  The new enhancements will enable customers to quickly identify root causes by viewing resource usage patterns and correlating telemetry data, proactively manage ECS resources using custom dashboards, track recent deployments and deployment failures, monitor resources across multiple accounts, and integrate with other CloudWatch services, like Application Signals and CloudWatch logs.  RELATED: AWS announces several updates to Amazon Bedrock and Amazon Q during re:Invent “This new capability will help reduce your mean time to de...

At AWS re:Invent, Amazon announced a number of new capabilities for generative AI and machine learning. New evaluations for Amazon Bedrock Two new evaluation capabilities have been added to Amazon Bedrock , which is the company’s platform for building generative AI applications using foundation models.  Amazon Bedrock Knowledge Bases now offer support for RAG evaluation (still in preview), which allows users to compare different configurations. Amazon Bedrock Model Evaluation now offers an LLM-as-a-judge feature (also in preview), which allows AI to perform tests and evaluate other models more quickly and cheaper than a human.  The evaluations provide a score of 0 to 1 and provide a natural language explanation for each score, as well as the rubric used to come to each score.  “These new capabilities make it easier to go into production by providing fast, automated evaluation of AI-powered applications, shortening feedback loops and speeding up improvements. These e...