EffectiveSoft’s Post

We all know GenAI is now reality and few organizations are front runners in leveraging this and same time most of the organizations started doing POC's, MVP 's as well identifying various business, operations, strategic use cases to leverage the power of this technology. In general, no org can build GenAI product without building right data foundation layer, So Data Engineering plays very vital role and going to be the backbone of modern AI systems, enabling the collection, transformation, and management of vast datasets that fuel Gen AI models. With Gen AI's growing reliance on vast, diverse datasets, mastering data engineering ensures the agility and power needed for advanced AI-driven insights. #dataamruth #GenAI #DataEngineering #Data

Business are about real people with real problems and sometimes and now more than ever the AI could be the tool to allowing people to reach their potential taking advantage of the tree ingredientes: 1)data bases & data science 2)GPUs - Hardawares, 3)models - software. Moreover the generative AI arms race has started, and the challenge it’s how to plug this tools to enhance the companies potential! From the first steps where human had machine assisted to human assisted and finally to AI / Automated. In KrediYA we support our product roadmap evolution with data analysis combine with AI and machine learning to exceed our customers expectations and main needs! Volcano Summit Michael Housman #understandingAIexplosion #innovation #fintech #creativity #audiencreation #digitalmarketing

Stay in the loop with all things AI! 🌀 From Microsoft’s sustainable data centers and the defining of open-source AI to AI’s increasing role in software development, we’ve got you covered. Plus, get some insightful analysis from Duru on distinguishing real AI from AI washing. Don’t miss out! 🙌 Subscribe to our bi-weekly newsletter for the latest insights, comprehensive analyses, and exclusive updates, all for free! 🎊 https://lnkd.in/dFV9cv3T #AI #TechNews #Sustainability #OpenSource #SoftwareDevelopment #AIInsights

💡 Awen Engineering: Our learnings on "Engineering around foundation models" and "How you can go to market 100x faster" We are serving enterprises over 2Mn API hits per month from our core AI infrastructure. We have deeply experienced the long tail of problems, and learnt from over 100+ discovery calls with global AI teams - 🔓 Three big problems bottleneck enterprise AI teams today: 1. Internal data curation and alignment 2. Engineering prompts that generate reliable outputs from foundation models - including confidence score calibration 3. Scaling evaluation dataset without spending hundreds of hours of manual efforts 🔑 Over the last 12 months, we have built out internal AI agents that help us reduce the time taken to solve each of these problems in hours now. 1. Upload your raw images and our internal services flag inconsistencies in tagging and classification 2. Update 1% tags across all your data - and send to our prompt AI service and auto generate the highest performing prompts 3. Auto generate your evaluation dataset with in built flags for class imbalance, dataset quality and anomalies 🚀 Why does this matter? You save months and millions by going to market 100x faster. #enterpriseAI #generativeAI #datacuration #reliability #timetomarket #speed We would love to hear your learnings in comments. Abhishek Borar Narender Nain Manikya Singh

Every time we sit down with enterprise CIOs and AI teams, the top of mind concern we hear is "Reliability" - how do we go from our use case to reliable AI as fast as we can? In our experience, reliable AI is extremely hard to engineer. There is no silver bullet - it is about the many long tail problems that you need to grind at for weeks and months. But, this is what makes enterprise AI extremely resource intensive today. We are building Awen to change this. We are building the core agentic infrastructure that helps AI teams ship reliable AI 100x faster - from data curation to running evals at scale. Experience more in our playground: https://lnkd.in/gFGnpcdR Abhishek Borar Himanshu Muchhal

Unlock the potential of intelligent technologies with Nth Phase Software Solutions. From AI's broad spectrum of mimicking cognitive functions to ML's focused approach in learning from data, we offer tailored solutions to propel your business into the future. Mail Us: info@nthphase.com website: www.nthphase.com #NthPhase #NthPhasesoftwareSolutions #itsolutions #Services #GenerativeAI #InnovationUnleashed #AIgenerative #CreativeAI #AIart #AIcreativity #ArtificialIntelligence #MachineLearningArt #GenerativeArt #ml

🚀 New Blog Post Alert! Discover how closed-source AI tools are reshaping the software development landscape. 🔗 Read more: [The Rise of Closed-Source AI Tool Integrations](https://lnkd.in/dUEAUxBV) #AI #Tech #SoftwareDevelopment

Maximize efficiency and savings in your Spark data pipelines with Chicory AI. The platform uses Generative AI to optimize your sparksql and transformations, helping you reclaim valuable resources and reduce costs. By addressing inefficiencies early in the development process, Chicory AI not only boosts developer productivity but also contributes to a more sustainable tech environment. Explore how you can enhance your data workflows at www.chicory.ai. Would love to get as much feedback as possible. #DataPipelines #SparkSQL #PySpark #CostSavings #Sustainability #ChicoryAI Chicory

Notes from my conversation with Demetrios Brinkmann on the evolving landscape of MLOps and AI integration: - The foundational role of data engineering is crucial for effective AI implementation, ensuring data is well-structured and reliable. - AI agents are set to revolutionize workflows by automating tasks, but their reliability and usability are still in development. - No-code/low-code tools are democratizing AI access, empowering non-technical users to leverage AI capabilities effectively. Special mention for professionals: - Embrace curiosity and iterative learning when engaging with AI systems to maximize their potential.

How much time per week do you spend fixing data issues? If the answer ranges anywhere between “a lot” and “a WHOLE lot”, good news – you’re not alone! Our research shows that 30% of total enterprise time is spent on non-value-add tasks that come from poor data quality and availability. Accurate data management is crucial, but data quality is a persistent issue for many large enterprise organizations. That’s why our QuantumBlack Labs team got together to build AI4DQ (”AI for Data Quality”), which uses AI to identify and fix data quality challenges at scale. Find out how it works: https://lnkd.in/g6awy4hQ #AIbyMcKinsey #ArtificialIntelligence #DataScience #Medium