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Community post by Annalisa Gennaro At the beginning of this year, I fell apart. I found myself in pieces, struggling to say a single word without bursting into tears. I had severe sleep issues, suffered from...
At Microsoft Ignite in November 2024, we announced offerings and resources to empower software companies in the landscape of AI-powered experiences. The post Unveiling the future of AI innovation for ISVs appeared first on Microsoft Azure Blog.
Buy with AWS enables you to seamlessly discover and purchase products available in AWS Marketplace from AWS Partner websites using your AWS account.
Amazon SageMaker HyperPod recipes help customers get started with training and fine-tuning popular publicly available foundation models, like Llama 3.1 405B, in just minutes with state-of-the-art performance.
Amazon commits $100M to empower education equity initiatives, enabling socially-minded organizations to create AI-powered digital learning solutions. This aims to reach underserved students globally through innovative platforms, apps, and assistants.
In this blog, we’ll explore how the unique features of Copilot enhance each element of DevEx across the SPACE framework. The post Drive efficient software development and boost DevEx with GitHub Copilot appeared first on Microsoft Azure Blog.
Find solutions to your most critical business challenges with ease. Amazon Q in QuickSight enables business users to perform complex scenario analysis up to 10x faster than spreadsheets.
Q Developer empowers non-ML experts to build ML models using natural language, enabling organizations to innovate faster with reduced time to market.
Build responsible AI applications - Safeguard them against harmful text and image content with configurable filters and thresholds.
Amazon Bedrock enhances generative AI data analysis with multimodal processing, graph modeling, and structured querying, accelerating AI application development.
Route requests and cache frequently used context in prompts to reduce latency and balance performance with cost efficiency.
Discover, test, and use over 100 emerging, and specialized foundation models with the tooling, security, and governance provided by Amazon Bedrock.
Unlock efficient large model training with SageMaker HyperPod flexible training plans - find optimal compute resources and complete training within timelines and budgets.
Enable priority-based resource allocation, fair-share utilization, and automated task preemption for optimal compute utilization across teams.
Member post originally published on Chronosphere’s blog by Carolyn King, Head of Community & Developer at Chronosphere New release: Fluent Bit v3.2 This week Fluent Bit maintainers are excited to announce the launch of Fluent Bit...
Red Hat this week made available a managed instance of the Ansible automation framework on the Amazon Web Services (AWS) cloud.
Exploring how PMs can incorporate DevOps methodologies into their workflows to drive efficiency, break down silos and enable rapid product iterations.
Firms considering transitioning from traditional research & development (R&D) environments to DevOps must consider implementation challenges. Otherwise, they risk failure — the repercussions of which are far-reaching. What challenges should they expect? More importantly, what should they do to prepare? Implementation Challenges Facing DevOps Teams Although operations and development teams work toward the same...
Amazon Web Services (AWS) today revealed it has extended the capabilities of its generative artificial intelligence (AI) tool.
Connect, discover, and govern data across silos with Amazon SageMaker Lakehouse's new data catalog and permissions capabilities, enabling centralized access and fine-grained controls.
Simplify data replication and ingestion from applications such as Salesforce, SAP, ServiceNow, and Zendesk, to Amazon SageMaker Lakehouse and Amazon Redshift.
Unifying data silos, Amazon SageMaker Lakehouse seamlessly integrates S3 data lakes and Redshift warehouses, enabling unified analytics and AI/ML on a single data copy through open Apache Iceberg APIs and fine-grained access controls.
Effortlessly analyze operational data in Amazon SageMaker Lakehouse, freeing developers from building custom pipelines and enabling seamless insights extraction.
Manage data and AI assets through a unified catalog, granular access controls, and a consistent policy enforcement. Establish trust via automation - boost productivity and innovation for data teams.
Realize visual traceability of data origins, transformations, and usage - bolstering trust, governance, and discoverability for strategic data-driven decisions.
Unify data engineering, analytics, and generative AI in a streamlined studio with enhanced capabilities of Amazon SageMaker.
Amazon Q Business extends productivity with generative AI-powered workflow automation capability and 50+ actions for enterprise efficiency, enabling seamless task execution across tools like ServiceNow, PagerDuty, and Asana.
New Amazon Q Business capabilities help ISVs integrate with the Amazon Q index to retrieve data from multiple sources through a single API and customize the design of their Amazon Q embedded assistant.
Enhancing coding productivity, Amazon Q Developer agents now offer capabilities for auto-generating documentation, conducting code reviews, and creating unit tests within IDEs and GitLab.
Unlock Linux's power with Amazon Q Developer's transformation capabilities for .NET porting – effortlessly modernize .NET applications from Windows to cross-platform .NET in your familiar IDE.
Amazon Q Developer streamlines large-scale transformations using generative AI agents supervised by teams through a unified web experience, accelerating .NET porting, mainframe modernization, and VMware migration.
Amazon Q Developer can now help you investigate and remediate operational issues quickly from anywhere in the AWS Management Console, accelerating the troubleshooting process for operators of all experience levels.
GitLab Duo with Amazon Q streamlines software development across tasks and teams by embedding advanced AI agent capabilities into the GitLab workflows developers already know.
Amazon Nova foundation models deliver frontier intelligence and industry leading price-performance, with support for text and multimodal intelligence, multimodal fine-tuning, and high-quality images and videos.
With multi-agent collaboration on Amazon Bedrock, developers can build, deploy, and manage multiple specialized agents working together seamlessly to tackle more intricate, multi-step workflows.