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Rathnakumar Udayakumar shared thisAI governance cannot begin after deployment. By then, the model may already be connected to sensitive data, business workflows, employees, and customers. A trusted enterprise AI program needs governance across the entire lifecycle. It begins with strategy and vision, ensuring every initiative supports a clear business objective and measurable outcome. From there, organizations need strong foundations: ↳ Data governance for quality, ownership, lineage, and access ↳ Privacy and compliance for sensitive and regulated information ↳ Identity and access management to control users, models, and tools ↳ Security guardrails against prompt injection, jailbreaks, and unsafe outputs ↳ Model governance for validation, explainability, approval, and versioning RAG systems also require knowledge governance. Models should retrieve only approved, current, relevant, and traceable organizational information. For high-risk decisions, human-in-the-loop controls must define when people review, approve, override, or stop an AI action. Governance must continue in production through: ↳ Risk assessments and control ownership ↳ Monitoring for hallucinations, drift, failures, cost, and latency ↳ Documented policies and development standards ↳ Responsible AI reviews for fairness and transparency ↳ Incident response plans for AI-related failures ↳ Continuous evaluation and improvement Tools can support each pillar, but tools alone do not create governance. Governance requires clear accountability, measurable controls, documented evidence, and escalation paths across business, legal, security, data, and engineering teams. The goal is not to slow AI adoption. It is to help the enterprise scale AI without losing trust, safety, compliance, or business value. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisA capable AI model can still produce a terrible product. Because production failures rarely begin with the model. They begin in the layers surrounding it. Open Atlas Summit 2026 brings AI engineers, founders, technology leaders, employers, and high-skilled immigrants together in Milpitas on August 21–22. Reserve your spot here: https://luma.com/8c4qusfp Here are 15 reasons promising AI systems break after launch: ↳ 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Incomplete, duplicated, outdated, or inaccurate data leads to unreliable outputs. ↳ 𝗪𝗲𝗮𝗸 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 The system retrieves irrelevant or incomplete information and forces the model to reason from misleading context. ↳ 𝗕𝗮𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 Noisy, excessive, or poorly ordered context hides the information that matters. ↳ 𝗣𝗼𝗼𝗿 𝗽𝗿𝗼𝗺𝗽𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 Vague or conflicting instructions create unpredictable behaviour. ↳ 𝗧𝗼𝗼𝗹-𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 Agents choose the wrong tool, send incorrect parameters, or fail to recover from errors. ↳ 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲𝗱 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 The model reports that work was completed even when no real action occurred. ↳ 𝗠𝗲𝗺𝗼𝗿𝘆 𝗲𝗿𝗿𝗼𝗿𝘀 Stale or irrelevant memory distorts decisions and creates inconsistent experiences. ↳ 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 Weak controls allow unsafe, restricted, or high-impact actions without proper checks. ↳ 𝗡𝗼 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 Without continuous testing, regressions in accuracy, relevance, and safety remain invisible. Prompt injection, high latency, cost explosions, missing observability, weak fallback strategies, and poor human experience create additional failure points. Production AI is not just about producing an answer. It is about retrieving the right context, taking the correct action, failing safely, and improving continuously. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisYou do not need an expensive degree to start learning AI. Some of the strongest learning resources are already available from the companies building the technology. Open Atlas Summit 2026 also brings AI professionals, founders, employers, and high-skilled immigrants together in Milpitas on August 21–22. Reserve your spot here: https://luma.com/8c4qusfp Here are 10 free AI learning platforms worth exploring: ↳ 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗦𝗸𝗶𝗹𝗹𝗷𝗮𝗿 Learn how Claude works and how to build practical applications with it. https://lnkd.in/e5fK7QUA ↳ 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Build a foundation in AI concepts, tools, and Google’s learning tracks. http://grow.google/ai ↳ 𝗠𝗲𝘁𝗮 𝗔𝗜 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 Explore open-source models, research, and practical AI education. https://lnkd.in/et6wz-ta ↳ 𝗡𝗩𝗜𝗗𝗜𝗔 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Study accelerated computing, AI infrastructure, and applied AI. https://lnkd.in/e8aHmFxc ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗮𝗿𝗻 Develop skills across AI, Copilot, Azure, and developer workflows. https://lnkd.in/ej85NeZc ↳ 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 Strengthen your understanding of AI foundations and practical use cases. http://academy.openai.com ↳ 𝗜𝗕𝗠 𝗦𝗸𝗶𝗹𝗹𝘀𝗕𝘂𝗶𝗹𝗱 Learn AI, data, and enterprise technology through structured content. http://skillsbuild.org ↳ 𝗔𝗪𝗦 𝗦𝗸𝗶𝗹𝗹 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 Explore cloud AI, machine learning, and generative AI on AWS. http://skillbuilder.aws ↳ 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴.𝗔𝗜 Follow structured courses covering machine learning, LLMs, and AI applications. http://deeplearning.ai ↳ 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 𝗟𝗲𝗮𝗿𝗻 Understand transformers, open-source models, and practical ML workflows. https://lnkd.in/eaiVCrvv Choose one platform, complete one learning track, and build a small project alongside it. Consistent practice will take you further than collecting dozens of unfinished courses. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB 🔔 Follow to stay updated on high-skilled immigration, jobs, and business.
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Rathnakumar Udayakumar shared thisYour first tech job does not begin with sending applications. It begins with choosing a clear direction and building evidence that you can do the work. A practical career journey looks like this: ↳ Pick one role based on your strengths, interests, and market demand. ↳ Learn the fundamentals before chasing every new framework or trend. ↳ Build the role-specific skills employers actually expect. ↳ Create 2–4 projects that solve realistic problems. ↳ Learn the tools used by real teams, including Git, GitHub, Docker, Jira, and VS Code. ↳ Turn projects into proof of work through code, demos, and measurable outcomes. ↳ Create a portfolio that makes your strongest work easy to understand. ↳ Tailor your resume to the skills, keywords, and impact required for each role. ↳ Build a consistent presence across LinkedIn, GitHub, and your portfolio. ↳ Connect with professionals, recruiters, and hiring teams. ↳ Apply selectively instead of sending the same application everywhere. ↳ Prepare for technical, behavioral, and project-based interviews. ↳ Explain what you built, why you made key decisions, and what results you achieved. ↳ Treat every rejection as feedback and improve the next attempt. Tools can support every stage, but tools alone will not build your career. The candidates who stand out combine relevant skills, visible proof, focused applications, and clear communication. You do not need years of experience to begin. You need a path, consistent execution, and enough proof to earn trust. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisAI in 2027 will be less about isolated tools and more about connected systems that act, collaborate, and improve continuously. For professionals building careers around this shift, Open Atlas Summit 2026 brings engineers, founders, technology leaders, employers, and high-skilled immigrants together in Milpitas on August 21–22. Reserve your spot here: https://luma.com/8c4qusfp These 15 trends show where AI is heading next: ↳ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗴𝗼𝗲𝘀 𝗺𝗮𝗶𝗻��𝘁𝗿𝗲𝗮𝗺 Autonomous systems will manage increasingly complex, multi-step work. ↳ 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 Specialized agents will coordinate, negotiate, and delegate like digital teams. ↳ 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 AI will understand individual context and act more proactively. ↳ 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 More products will be designed around AI from the beginning rather than adding it later. ↳ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗔𝗜 Streaming responses, live reasoning, and instant decision-making will become standard expectations. ↳ 𝗦𝗺𝗮𝗹𝗹𝗲𝗿, 𝗳𝗮𝘀𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 Specialized models will deliver strong performance with lower cost and latency. Other defining shifts include AI-powered robotics, edge AI, scientific discovery, cybersecurity, synthetic data, stronger governance, and deeper human-AI collaboration. The biggest change will not be one breakthrough model. It will be AI becoming an invisible layer across products, workflows, decisions, and entire industries. Which of these trends do you believe will have the greatest impact by 2027? Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisAn enterprise copilot is more than a chatbot. It is a connected system that understands context, retrieves trusted knowledge, takes action, and improves continuously. A strong architecture starts with nine building blocks: ↳ Define the use case Identify users, workflows, tasks, success metrics, and feasibility. Clear scope keeps it tied to business value. ↳ Connect enterprise data Provide authorized access to documents, databases, CRM, ERP, and internal APIs. ↳ Build the knowledge layer Ingest, clean, chunk, embed, index, retrieve, and re-rank information so responses stay grounded. ↳ Select the right LLM Compare reasoning, context window, latency, security, and cost. ↳ Add context and memory Use identity, preferences, history, workflow state, and retrieved knowledge. ↳ Connect tools and actions Let the copilot search records, update systems, create tickets, trigger workflows, and complete approved tasks. ↳ Add security and guardrails Apply authentication, role-based access, PII protection, prompt-injection defenses, approvals, and audit logs. ↳ Orchestrate and deploy Connect retrieval, reasoning, tool calls, approvals, and responses into a reliable workflow. ↳ Evaluate and observe Track quality, groundedness, latency, cost, tool success, and safety failures. The goal is not simply to answer questions. It is to build a trusted enterprise copilot that can understand, act, and improve. Which layer is hardest to get right in your organization? Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisProud to receive this special message from U.S. Congresswoman Zoe Lofgren, Representative for California’s 18th Congressional District and a longstanding leader on immigration policy. Congresswoman Lofgren will be present virtually at the Open Atlas Summit 2026, bringing her experience, guidance and perspective directly to our community. In this address, she reflects on her background and shares her hope for reforms that allow talented people from around the world to come to America, contribute and succeed based on merit. That vision is closely aligned with what we are building through the Open Atlas Summit. On August 21 and 22, immigrants, founders, professionals, investors and policy leaders will come together to find answers, build relationships and unlock new possibilities. 🎟️ Book your Summit ticket before seats fill up. Book Summit Tickets: https://luma.com/8c4qusfp Get Job Fair Passes: https://lnkd.in/dssuJrAQ #OpenAtlasSummit #ZoeLofgren #Immigration #ImmigrantSuccess #GlobalTalent
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Rathnakumar Udayakumar shared thisProduction AI is not just a model. It is an entire system. A strong LLM can generate impressive answers, but without the right infrastructure around it, the experience becomes slow, insecure, expensive, and unreliable. A production-ready AI stack needs 15 connected components: ↳ A user interface where people submit requests ↳ Authentication and authorization to protect access ↳ An API gateway to manage routing, limits, and security ↳ A model router to select the best model for each task ↳ The LLM that performs reasoning and generation ↳ A RAG pipeline that grounds answers in trusted knowledge ↳ A vector database for semantic retrieval ↳ A memory layer that preserves context ↳ Tool calling and MCP to connect agents with real systems ↳ A workflow orchestrator for multi-step execution ↳ Guardrails to reduce unsafe outputs, leakage, and prompt attacks ↳ Monitoring to track latency, cost, errors, and reliability ↳ Caching to avoid repeated computation ↳ Deployment and scaling for dependable production use ↳ Feedback loops that improve prompts and workflows The value comes from how these layers work together. The model may power the intelligence, but infrastructure determines whether the system can be trusted, governed, scaled, and improved. That is the difference between an AI demo that looks impressive and an AI product that survives real users, real data, and real business pressure. Which layer is currently the weakest in your AI architecture? Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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Rathnakumar Udayakumar shared thisEnterprise AI works like a brain. It creates value when every layer connects, learns, and improves together. At the core are foundation models such as Claude, Llama, Mistral, and other LLMs. They provide language understanding and generation, but they are only one part of the system. ↳ Reasoning and planning help AI analyze context, make decisions, create steps, and adapt when conditions change. ↳ Agents and orchestration break complex work into tasks, coordinate agents, connect workflows, and involve humans in key decisions. ↳ Memory and knowledge provide short-term context, long-term recall, vector search, and knowledge graphs. ↳ RAG and context engineering retrieve the right information, manage prompts, re-rank results, and build context for accurate responses. ↳ Infrastructure and data provide compute, storage, networking, and pipelines that keep the system scalable. These capabilities create business value through assistants, copilots, automation, and decision intelligence. A continuous improvement loop connects: Data and feedback → Evaluation → Learning → Better decisions → Better outcomes Security, governance, guardrails, observability, privacy, compliance, and cost controls must operate across every layer. Enterprise AI is not a single model or tool. It is a connected system where models think, agents act, memory informs, infrastructure supports, and feedback improves performance. Which layer of your enterprise AI system needs the most attention today? Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
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4h
Such a valuable share Anjali!
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Rathnakumar Udayakumar liked thisRathnakumar Udayakumar liked thisAI Gateway Architecture on Microsoft Azure - Simplified Blueprint 🚀 Most teams jump into AI without a clear architecture. That’s where things break. Here’s a practical blueprint to build a scalable, secure AI gateway on Microsoft Azure: 🔹 Identity & Security First Use Azure AD (Entra), Key Vault & Managed Identity to keep everything secure by design. 🔹 Central Gateway with APIM Azure API Management acts as the control layer—routing, throttling, and governing all AI requests. 🔹 Event-Driven Processing Azure Event Hub enables async + batch workloads for high-scale scenarios. 🔹 Seamless AI Integration Connect to Azure OpenAI PTUs or LLM deployments via secure network layers. 🔹 Observability Built-In Azure Monitor + Logs + Metrics → full visibility into usage, performance, and failures. 🔹 Actionable Insights Dashboards, alerts, and automated actions ensure reliability at scale. 💡 Why this matters? Because AI systems without governance = chaos. This architecture ensures control, scalability, and production readiness. If you're building AI systems on Azure, this is the layer most people miss. 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗝𝗼𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 👉 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 now → https://avsl.beehiiv.com/ Follow Aiswarya Venkitesh for more AI insights.
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Rathnakumar Udayakumar liked thisRathnakumar Udayakumar liked thisA job loss can become an immigration deadline overnight. The first step is not panic. It is understanding what your current status allows, which date controls your stay, and what immigration progress may still be protected. Start with four questions: 1. How much time do you have? Confirm your visa classification, final employment date, I-94 expiration, and grace-period eligibility. Certain employment-based nonimmigrant workers may receive a discretionary grace period of up to 60 days—or until their authorized validity ends, whichever is shorter. 2. Can you move to another employer? A new role must fit your immigration status, and the employer must be prepared to file the appropriate petition. Eligible H-1B workers may be able to begin working after a new employer properly files the required petition. 3. What happens to your Green Card process? A PERM case may need reassessment. An approved I-140 can preserve the priority date in many circumstances. When an employment-based I-485 has been pending for at least 180 days, portability may be available for a same or similar role. 4. What is Plan B? Depending on individual eligibility, the next path could involve a new employer, change of status, dependent status, or a profile-based immigration option. Preparation matters before a layoff happens. Keep your I-94, approval notices, passport, pay records, PERM and I-140 documents, I-485 records, and attorney contact organized. A layoff is an employment event. It should not become an immigration emergency because the first deadline was missed. Know your status. Know your deadline. Know what can be preserved. Then choose the next path with qualified legal guidance. This is general educational information, not legal advice. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business #H1B #ImmigrationJourney #GreenCard #EB1A #EB5 #USImmigration
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Rathnakumar Udayakumar reacted on thisAI governance cannot begin after deployment. By then, the model may already be connected to sensitive data, business workflows, employees, and customers. A trusted enterprise AI program needs governance across the entire lifecycle. It begins with strategy and vision, ensuring every initiative supports a clear business objective and measurable outcome. From there, organizations need strong foundations: ↳ Data governance for quality, ownership, lineage, and access ↳ Privacy and compliance for sensitive and regulated information ↳ Identity and access management to control users, models, and tools ↳ Security guardrails against prompt injection, jailbreaks, and unsafe outputs ↳ Model governance for validation, explainability, approval, and versioning RAG systems also require knowledge governance. Models should retrieve only approved, current, relevant, and traceable organizational information. For high-risk decisions, human-in-the-loop controls must define when people review, approve, override, or stop an AI action. Governance must continue in production through: ↳ Risk assessments and control ownership ↳ Monitoring for hallucinations, drift, failures, cost, and latency ↳ Documented policies and development standards ↳ Responsible AI reviews for fairness and transparency ↳ Incident response plans for AI-related failures ↳ Continuous evaluation and improvement Tools can support each pillar, but tools alone do not create governance. Governance requires clear accountability, measurable controls, documented evidence, and escalation paths across business, legal, security, data, and engineering teams. The goal is not to slow AI adoption. It is to help the enterprise scale AI without losing trust, safety, compliance, or business value. Want to learn more about O1, EB1A and EB5? Schedule a free consultation - https://lnkd.in/gEf3y7JB Meet sponsor-friendly employers, top attorneys, VCs, creators, and O1 / EB1A / EB5 recipients at the largest summit for high-skilled immigrants - https://luma.com/8c4qusfp 🔔 Follow to stay updated on high-skilled immigration, jobs, and business
Experience & Education
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Open Atlas
View Rathnakumar’s full experience
See their title, tenure and more.
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Licenses & Certifications
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EMC Academic Associate, Data Science and Big Data Analytics
EMC
IssuedCredential ID QVS3YK961JBQ1Z3T -
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Business English Certification
University of Cambridge
IssuedCredential ID 0033132268 -
Certificate Program on Business Plan Formulation
Ichiban Academy
IssuedCredential ID IA2010-CPBPF-0001-S0005
Volunteer Experience
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ELeader and Core Organizer
National Entrepreneurship Network
- 4 years 1 month
Core member, in charge of maintaining smooth functioning of the club having about 40 different member colleges, planning and execution of meetings and events, head public relationship
Publications
Patents
Projects
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Fixnix
See projectPAAS product, customized cloud-based GRC solution using blockchain for SME Sector.
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Vue.ai
See projectSAAS product, a Personalized retail solution using artificial intelligence for Retail and Hospitality Industry.
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Yoda
See projectSAAS product, Yoda is an indispensable personal sales assistant. Yoda’s patented technology prioritizes prospects and personalizes sales messaging for enterprises targeting SME.
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Maya
See projectPAAS product, Maya is a choice engine that pivots on insightful personal tastes, to create personalized digital experiences for every enterprise customer.
Honors & Awards
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Best Student E-Leader Award
TIE
Languages
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English
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Tamil
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Hindi
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Neha Gaandhii
🎙️ Training Interrupted with AI — Episode 1 is out! Our first topic came straight 𝗳𝗿𝗼𝗺 𝗮 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗽𝗼𝗹𝗹: 𝗜𝘀 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗴𝗿𝗲𝗮𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲? We wanted to give you: 𝗥𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀. 𝗥𝗲𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀. 𝗥𝗲𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴. So, we spoke to experienced practitioners who actually get their hands dirty with AI every single day— Apurva Kumar & Rahul. If you work in L&D, training, or content creation—this one’s for you. 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗽𝗼𝗱𝗰𝗮𝘀𝘁 𝗮𝘁: https://lnkd.in/gW_tK6xh #LXDGuild #training #GenAI #learning
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Preeti Cholleti
Collaborative Declaration on the U.S.-India AI Partnership Opportunity https://lnkd.in/djdJUXtW Unlocking the Future: U.S.-India AI Opportunity Partnership The U.S. and India have formalized a groundbreaking AI partnership under the Pax Silica Declaration, setting the stage for an innovative future. This collaboration emphasizes the shared commitment to build a robust innovation ecosystem, crucial in defining the 21st century. Key Highlights: Pro-Innovation Regulation: Both nations plan to mainstream frameworks that foster technological growth and investment. Strengthening the AI Infrastructure: Collaboration on reliable energy and essential minerals is prioritized to support future technology. Driving Free Enterprise: The partnership encourages private sector innovation, promoting venture capital and cross-border R&D to empower entrepreneurs. Together, these efforts signify an alignment not just for defense, but for shared prosperity. The aim is to create an AI landscape that enhances societies and reflects democratic values. 💡 Join the conversation! Share your thoughts on how this partnership can lead to a more prosperous AI future. Source link https://lnkd.in/djdJUXtW
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arnav anand
#KRAFTON India announced a series of strategic collaborations with leading global technology and analytics platforms to enhance its support infrastructure for startups under the KRAFTON India Gaming Incubator (KIGI). These collaborations provide early-stage gaming studios access to vital tools and services needed to develop, test, scale, and market their games more effectively. https://lnkd.in/ghBh5eZJ
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