Akvelon, Inc.的动态

As #software grows more complex, traditional observability struggles to keep up. ?? Traditional #Observability: ? Fragmented #logs and #traces. ? Slow issue resolution due to limited real-time capabilities. ?? Observability 2.0: ? Centralizes #telemetry data for better insights. ? Utilizes #AI to detect anomalies and predict issues. ? Provides actionable insights for faster decision-making. Upgrade to smarter, modern observability. Read Middleware ’s blog: https://lnkd.in/dCfbHMtH #Observability #SoftwareDevelopment #DevOps #MachineLearning #TechInnovation

Setting up for #GenAI... Leverage and apply the best practice ways of working from digital software and data management. Items like: - Enterprise 'prompt store' > Similar to a 'code library' or 'feature store'. - Standardisation of naming conventions and versioning for 'prompts'. - Standardisation on 'prompt management' methods > Similar to 'master data management' e.g. inclusion in a dictionary/ glossary/ catalogue and lineage. - Guidance on which model when, optimised for cost-benefit/ value for outcome > so that for a specific use case there is direction on which model (ChatGPT, Claude, COPILOT, Titan, Llama...) and which variant of the model family is best fit. [like optimising your use of, and spend on, services in AWS and MS Azure] - Guidance on when testing is required and what test cases need to be performed and passed > so that for a specific use case there is confirmation that it works in production as expected. - Change release process for when 'prompts' are updated and enhanced. - Governance policies and constructs so that risk, privacy, regulatory compliance, brand impact, etc are appropriately managed for the benefit of customer and the enterprise. - Issue triage, management and remediation process so that when things go wrong (and at some time they will) they can be fixed. - Change management, including documentation and training for your people so that 'prompts' are appropriately and fully utilised. Consider using tooling like this https://promptleo.com/ (I am experimenting with this one, am sure there are others). Remember that getting the foundations right will allow you to go sky high with the opportunity that #GenAI offers! #leadership #strategytoexecution #GenAI #ChatGPT #ClaudeAI #thinkingoutsidethebox #digitalbusiness #digitaltransformation #datatransformation #aifirst #aitransformation #businesstransformation #enterprisetransformation #CCO #CMO #CDO #CDAO #buildingthefuture #daretolead #thinkingdifferently

?????????????????? has introduced the??????????? ?????????????? ???????????????? (??????), an open-source standard designed to address a key limitation of AI chatbots: their isolation from real-world data. By enabling seamless, two-way connections between AI applications and diverse data sources, MCP aims to enhance the relevance, accuracy, and functionality of AI systems in practical contexts. ???????? ???? ?????? ?????? ?????? ???????? ???? ????????? MCP is a protocol that allows developers to build standardized connections between AI assistants and the systems where data resides. Here’s how it works: ?? ?????? ??????????????: These expose data from tools like Google Drive, Slack, GitHub, and other repositories. ?? ?????? ??????????????: AI applications (e.g., chatbots) connect to these servers to access and utilize the data. This approach eliminates the need for custom connectors for each data source, replacing fragmented integrations with a scalable and sustainable architecture. Developers can begin using pre-built MCP servers today or deploy remote production servers for enterprise-wide use. https://lnkd.in/d2mM8hxt #AIInnovation #DataIntegration #OpenSource #LLM #GenAI #AIChatbots #FutureOfAI #TechLeadership ????

Our CIO Tiago Azevedo explains how #lowcode tools simplify the development process and enable businesses to customize and deploy #GenAI solutions at speed: https://bit.ly/3DWhKsC

?? AI Agents are Revolutionizing How We Work! ?? AI agents are transforming industries by automating complex tasks, making data-driven decisions, and interacting seamlessly with humans and other systems. From personal assistants to powerful enterprise tools, these agents are pushing the boundaries of what's possible in technology. ?? Software Engineering ?? DevOps ?? Data Engineering ?? DataOps ?? Machine Learning ?? MLOps ?? AI Agent Development ?? AgentOps As technology evolves, so do our frameworks for operational excellence. With AgentOps, we're introducing a streamlined approach for building, deploying, and scaling AI agents, ensuring security, performance, and resilience from the ground up. Just as in DevSecOps, a Security-First approach is essential in AgentOps—integrating security from the earliest design stages. Key Components of an AgentOps Framework: 1. Compliance & Security 2. Observability 3. Automated Testing 4. CI/CD Pipelines However, observability in AgentOps comes with some unique requirements: LLM Costs : Track and optimize model inference expenses. Token Usage: Monitor token consumption in multi-agent systems. Latency: Track response times across each agent's steps. Tool Usage Metrics: Trace agents' tool usage and measure latency. Session Reproducibility: Maintain session tracking for traceability. Long-Running Agent Interactions: Manage extended sessions effectively. Infinite Loop Detection: Guard against runaway agentic interactions. AgentOps is all about deploying secure, reliable AI agents that operate seamlessly in production. Stay tuned for the next post, where I’ll dive into more of the unique requirements in this exciting new framework! #AgentOps #AIAgents #MLOps #Security #DevSecOps #AIFramework

?? Model Context Protocol (MCP): The Future of AI Connectivity ?? Model Context Protocol (MCP) being open-sourced, and it’s a major leap forward for AI. MCP is a universal standard that connects AI assistants with data repositories, business tools, and development environments, making AI more context-aware and useful. ??? AI tools are isolated by information silos and custom integrations, limiting their potential. Each new data source has meant extra work for developers—until now. ?? MCP provides a universal protocol to securely link AI systems to data sources. Developers can build MCP servers to expose their data or create AI tools (MCP clients) to interact with these servers. Pre-built servers for platforms like Google Drive, Slack, and GitHub make it even easier. ?? Early adopters like Block and Apollo are already reaping the benefits, while companies like Replit and Sourcegraph are using MCP to improve coding tools, enabling smarter AI-driven development. MCP is open-source, collaborative, and designed to scale. Developers can start exploring today through pre-built servers, Quickstart guides, and an open repository. This feels like a game-changer for AI integration—excited to see where this leads! #AI #OpenSource #Innovation #ModelContextProtocol #Tech https://lnkd.in/gBcP2PvE

We asked you to vote on the most important emerging technology in #DevOps this year. ???? More than half of you went for 'Integration of AI services'. It's clear that AI-driven automation, analytics, and decision-making capabilities are changing the way we can build, test, deploy, and monitor software applications. #AI #PollResults #TechTalk #Developer

The future of GenAI may pivot towards smaller language models tailored for enterprise applications, promising agility, customization, and enhanced security. As companies embrace GenAI to drive efficiencies and productivity, they face challenges related to elevated expenses and intricate processes. #InstructLab #RedHat #RHELAI

Our CIO Tiago Azevedo explains how #lowcode tools simplify the development process and enable businesses to customize and deploy #GenerativeAI solutions at speed.

Our CIO Tiago Azevedo explains how #lowcode tools simplify the development process and enable businesses to customize and deploy #GenerativeAI solutions at speed.