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
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As we wrap up 2024, we're excited to showcase Akvelon's articles that resonated most with our readers. These 10 pieces, which include AI breakthroughs and development best practices for engineers of all levels, sparked the most significant engagement and discussion in our tech community. Let's revisit the insights that captured your attention this year before we enter the new year and publish more things worth checking out: 1) How DevOps Engineering Services Accelerate Fintech Success: https://lnkd.in/eFUxrkan 2) AI-Powered Medical Form Automation for Reducing After-Hours Work: https://lnkd.in/eMuG-7Fp 3) AI for PII Data Management in Fintech: How to balance innovation and PII protection: https://lnkd.in/daa_byUD 4) Top 5 Headless CMS in 2024: A comprehensive guide: https://lnkd.in/dPvfPC9T 5) How to Navigate the Data Maze and Extract Value from Unstructured Data Using LLMs: https://lnkd.in/dWR-agiK 6) Testing Out-of-the-Box LLMs in Translation: Results and outcomes: https://lnkd.in/eBW-_htA 7) How to Evaluate Software Outsourcing Companies and Find the Right One: https://lnkd.in/dAEBtt3Q 8) Over 100K Hours Spent Using GitHub Copilot: What's our speed boost? https://lnkd.in/dErRN9t3 9) AI Risks Drawing Attention of the U.S. Policymakers and Examples of Responsible AI: https://lnkd.in/dx5frhNJ 10) 6x Faster API Testing with AI-Powered API Testing Tool: https://lnkd.in/d3t7R6CU
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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
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?? 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
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How AI can automate CI/CD pipelines, code review and vulnerability remediation . #aiml #llm #codereview #vulnerability "The integration of Artificial Intelligence (AI) into Continuous Integration and Continuous Deployment (CI/CD) pipelines is revolutionizing software development processes. AI-powered automation is streamlining and optimizing the entire software delivery lifecycle, enabling teams to deliver high-quality applications faster and more efficiently. One of the key areas where AI shines is in automating code reviews and testing. Machine learning models can analyze code changes, identify potential bugs, security vulnerabilities, and code quality issues, providing valuable insights to developers before the code is even merged. This proactive approach reduces the risk of introducing defects and ensures a smoother deployment process. Furthermore, AI can optimize resource allocation and infrastructure management within CI/CD pipelines. By analyzing historical data and real-time metrics, intelligent systems can dynamically scale computing resources, ensuring efficient utilization and minimizing costs. AI-driven automation also plays a crucial role in monitoring and incident management. Advanced algorithms can detect anomalies, predict potential issues, and even suggest remediation steps, minimizing downtime and ensuring application stability. As AI capabilities continue to evolve, we can expect to see even more innovative applications in the CI/CD domain, such as self-healing pipelines, intelligent test case generation, and automated performance optimization. ?? Embracing AI in CI/CD pipelines not only accelerates software delivery but also enhances quality, reliability, and overall operational efficiency. Organizations that adopt this cutting-edge technology will gain a competitive edge in the fast-paced world of software development. #follow Bijan Dash for insights, help and updated #ai information #CICD #ArtificialIntelligence #DevOps #SoftwareDevelopment #AutomatedTesting #CodeReview #ResourceOptimization #IncidentManagement #InnovativeTools"
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?? Governance Policy + AI/ML + Web3 + DevOps = Policy as Code (PaC) ???? Transform policies into version-controlled, collaborative, and transparent code. With AI and open-source tools, PaC makes policy development continuous, data-driven, and responsive. ?? Analyze | ?? Iterate | ?? Engage | ?? Deploy Let’s make policy-making smarter, faster, and more democratic—one line of code at a time. ??? #PolicyAsCode #GovTech #AI #ML #OpenSource #Innovation
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The AIOps market has accelerated dramatically in recent years and is expected to surpass US $64 billion by 2028. However, rapid market expansion has created customer confusion and other challenges. #AIOps Open integration addresses these issues by uniting disparate AIOps solutions on a vendor- and technology-neutral platform. Engineers can use the aggregated insights to: Identify patterns and trends that isolated AIOps can’t due to limited coverage Detect, correlate and correct root causes faster than with niche AIOps solutions Automate workflows and orchestration to help reduce labor costs and overall IT expenses Analyze security incidents and provide pattern-based predictions, anomaly detection and remediation
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AI Tools in Software Testing: Boon and Bane **Boon:** - Enhanced Efficiency: AI automates repetitive tasks like test case generation, execution, and defect identification, saving time and reducing human error. - Improved Test Coverage: Machine learning can analyze vast datasets to identify patterns and edge cases that might go unnoticed. - Faster Feedback: AI-driven continuous testing accelerates delivery pipelines in DevOps, ensuring quicker go-to-market. - Predictive Analytics: AI helps forecast potential system failures, enabling proactive issue resolution. **Bane:** - Initial Setup Complexity: Implementing AI tools often requires significant effort, training, and resources. - Skill Gaps: Teams may lack expertise in AI-specific testing tools and c can sometimes misinterpret scenarios, leading to unreliable results. - Dependence on Quality Data: Poor-quality data fed into AI models can lead to inaccurate testing outputs. #AITesting #SoftwareTesting #AutomationTools #TechBoonAndBane #TestingTrends #QAInnovation #MachineLearning #DevOpsTesting #QualityAssurance #DigitalTransformation
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What challenges are you facing with your AI project?
?? Did you know that 85% of AI projects fail to deliver business value? Despite the growing investment in AI, many projects stumble between proof-of-concept and full deployment. In this carousel, we explore the key reasons why so many AI initiatives struggle and how to overcome these challenges: ?? Difficulty in transitioning from proof-of-concept to production?? ??? Engineering skill gaps, especially in MLOps and DevOps?? ?? Lack of a scalable infrastructure At CAIDEL, we specialize in transforming AI prototypes into scalable, production-ready software. Our team ensures that your AI solution doesn’t just stop at an idea but becomes a valuable asset for your business. ?? Swipe through to learn more and share the challenges you’re facing in the comments below! #AI #MLOps #DevOps #AIProjects #BusinessValue #TechInnovation #CAIDEL #AIDevelopment
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?? Did you know that 85% of AI projects fail to deliver business value? Despite the growing investment in AI, many projects stumble between proof-of-concept and full deployment. In this carousel, we explore the key reasons why so many AI initiatives struggle and how to overcome these challenges: ?? Difficulty in transitioning from proof-of-concept to production?? ??? Engineering skill gaps, especially in MLOps and DevOps?? ?? Lack of a scalable infrastructure At CAIDEL, we specialize in transforming AI prototypes into scalable, production-ready software. Our team ensures that your AI solution doesn’t just stop at an idea but becomes a valuable asset for your business. ?? Swipe through to learn more and share the challenges you’re facing in the comments below! #AI #MLOps #DevOps #AIProjects #BusinessValue #TechInnovation #CAIDEL #AIDevelopment
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更多文章
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#13. The visible rituals are only the small tip of Agile [The playbook for HOW! A journey to sustainable performance in the digital age]
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#12. It's much more than "test & learn" [The playbook for HOW! A journey to sustainable performance in the digital age]
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#11. Truly reorganise end-to-end around customer as your priority [The playbook for HOW! A journey to sustainable performance in the digital age]
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