How to turn AI experiments into business value with Knapsack

💡 What if MLOps could be as easy to navigate as a map? I just created MLOps Version 1 — a mind map that organizes everything from data pipelines to model drift, CI/CD to ethical AI — hierarchically. The goal? Turn complexity into clarity. MLOps Mind map with Xmind : https://lnkd.in/eT2mwbmD This is only Version 1. Next up: visuals, workflows, and actionable insights. MLOps doesn’t have to be messy. You just need the right map. 🗺️ #MLOps #AI #MachineLearning #DataScience #MindMap #TechLeadership ♻️ Repost if you find it useful. ➕ Follow me Praveen Subramani for more such content in AI.

Machine Learning Engineers do more than build models — they shape how data becomes intelligent action. Their role blends technical depth with strategic vision, ensuring every algorithm aligns with real-world outcomes. From data pipelines to deployment, their work defines the bridge between AI theory and business impact. Read more: https://bit.ly/3J43nVR #AI #DecentralizedAI #EdgeComputing #LLMs #OpenAI #DeepMind #ARTiBA

Building AI agents today often means juggling way too many tools. One for text, one for images, one for embeddings, another for metadata, and then stitching it all together to get a simple answer. With #ApertureDB no more wrangling data silos. Just one smart, unified layer your AI agents can reason over. #AI #MultimodalAI #AIagents #VectorDatabase #ApertureDB #GenAI

I came across a really useful AI prompting framework on YouTube today — it’s called A.I.M., and it instantly clicked for me. It stands for: Actor – who the AI should “be” Input – what information or context you’re giving it Mission – what outcome you want I have been unknowingly using this prompt architecture since 2023, but I never associated it with this Acronym. Using this prompt framework gives you AI responses that are sharper, more relevant, and closer to what you actually need. If you work with AI tools regularly, check out this framework — it’s transformative. Here’s the video that explains it well: https://lnkd.in/gRZGRzjJ

At Dreamforce, our COO Ron Costa sat down with Rafaela Guimarães from Socialis to explore a question that sits at the heart of every modern RevOps team: 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝘁𝗿𝘂𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗮𝗿𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀? The answer, as Ron explains, starts with structured data: the foundation that turns disconnected systems into a single source of truth. When data is clean, unified, and contextual, AI doesn’t just respond → it reasons, predicts, and acts with precision. This is the shift from manual management to autonomous revenue. 🎥 Watch Ron’s insights starting at 6:37 → https://lnkd.in/gC37M9DE #SAASTEPS #Dreamforce #RevenueOperations #AI #DataStrategy #AutonomousRevenue #RevOps

🚀 Excited to share progress on a side-project I’ve been building: a Payslip Analyzer designed to make payslips easier to read and verify. It blends classic OCR with modern ML models to extract data accurately, learn patterns over time, and surface meaningful insights. The tool is still in development, but the mission stays simple: Give people clarity, confidence, and transparency in understanding their payslips. Keep following for future updates — more features and smarter insights on the way. #ML #MachineLearning #GenAI #GenerativeAI #AI

A great AI outcome starts with a great prompt. That’s why Insentra AI helps teams master the art of prompt engineering — translating business challenges into clear, powerful instructions that generative AI understands. With the right approach, AI becomes more than a tool — it becomes a creative collaborator that scales ideas and accelerates outcomes. The question isn’t what AI can do — it’s how well you can ask it to. Curious to dive deeper? Check out this great blog: https://lnkd.in/g9PXz7Gz #InsentraAI #GenerativeAI #PromptEngineering #AIInnovation #FutureOfWork

Even the most optimized landing page won’t appear in AI results if it doesn’t feel believable. My latest blog breaks down how to transform your site into a source AI can trust, from schema markup to visual proof and author transparency. 🧠 Read here: https://lnkd.in/g_QPrFNp

🚀 Just published a simple guide explaining one of the biggest questions in AI: 👉 RAG vs Fine-Tuning — What’s the difference and when to use which? 🔹 RAG = Adds knowledge at runtime 🔹 Fine-Tuning = Teaches the model new behavior 🔹 RAG → Best for PDFs, policies, changing data 🔹 Fine-Tuning → Best for consistent output & style 🔹 Both together → Smart + Accurate + Reliable AI If you're building with LLMs, this is a must-know. #AI #LLM #RAG #FineTuning #GenerativeAI

The data landscape is unrecognizable compared to just a few years ago. With the explosion of AI, LLMs, and agentic workflows, legacy data platforms are making it difficult to keep up with innovation. ⚙️ Instead of building platforms solely focused on data storage, businesses need to be building truly scalable, future-proof platforms that can actively support the next generation of AI agents. 🤖 Get in touch to learn more. 👇 https://lnkd.in/eCKCxeWA #DataArchitecture #AI #DataPlatform #FutureProof #LLMs #AgenticWorkflows