How to build an AI MVP with the 3-Resource Rule

Struggling to deploy, scale, and manage machine learning models efficiently and cost-effectively? This challenge can slow down your AI projects, increase operational costs, and hinder innovation in a competitive landscape. As ML workloads grow, the need for a flexible, secure, and scalable cloud platform becomes crucial for data science teams and AI developers. Runpod is the cloud built specifically for AI and machine learning, providing powerful, cost-effective GPU infrastructure that streamlines deployment, scaling, and management of ML workloads. ✔️ Spin up GPU pods in seconds with ultra-fast cold start times, reducing setup delays from minutes to milliseconds ✔️ Deploy any container on a secure, global cloud with support for multiple image repositories and custom templates ✔️ Effortlessly scale ML inference and training workloads with serverless autoscaling, real-time analytics, and zero operations overhead Get Started with Runpod Today - https://lnkd.in/dg8xEnvT

The AI advantage isn’t custom code—it’s knowing what NOT to build. Your competitors aren’t failing at AI because of technology. They’re failing at strategy. 🎯 I just watched another company spend $500K building a custom AI model that AWS offers for $200/month. Here’s what nobody tells you about AI implementation: The expensive mistake: Building everything from scratch because it feels more “innovative” The smart play: → Use Azure’s pre-trained models for document processing → Leverage GCP’s AutoML for quick prototyping→ Deploy AWS SageMaker when you need scale The companies winning with AI aren’t the ones with the biggest budgets. They’re the ones who know which problems to solve vs. which solutions to buy. Last month, a client cut their data processing time from 3 days to 4 hours. Not with custom code. With the right cloud service configured correctly. Smart AI strategy isn’t about being cutting-edge. It’s about being strategic with what already exists. What’s the biggest AI misconception you’ve encountered? #ArtificialIntelligence #CloudStrategy #BusinessEfficiency #DigitalTransformation #TechLeadership #AWS #Azure #GoogleCloud #Innovation

🤔 Why choosing: Use Vertex AI Agent Engine with BOTH Cloud Run & GKE! One of the most common questions I received is: Can I use Vertex AI Agent Engine services like Memory Bank with my agent running on GKE or Cloud Run? The answer is: YES! You don't have to choose between fully managed memory, or a session and your preferred runtime. You can deploy your agent on Cloud Run and GKE. Instead of managing these agent ops services yourself, you can use Vertex AI Agent Engine. My colleague Vlad Kolesnikov just shared two new, hands-on tutorials that show exactly how to build AI agents with the Agent Development Kit (ADK) and use Vertex AI Agent Engine for its managed Sessions and Memory Bank. Check out the full code in the comments 👇 And stay tuned! We are working on more samples and better documentation to integrate other Agent Engine services with Cloud Run and GKE. #GoogleCloud #VertexAI #AI #GenerativeAI #GKE #CloudRun #Kubernetes #Serverless #LLMs #MLOps #Developers #ADK

Build a Local AI Chat with NLWeb, Azure AI Search, and Azure AI Foundry Ever wanted to run your own local AI chat that connects directly to your own data - powered by Azure AI Search, Azure AI Foundry, and NLWeb? In this first part of the series, I walk through how to set up the environment, prepare your data, and build the foundation for a local AI chat app that integrates tightly with the Azure ecosystem and .NET. Read it here: Build a Local AI Chat with NLWeb, Azure AI Search, and Azure AI Foundry (Part 1) https://lnkd.in/gUjp2sEH

LLM-Evalkit is a lightweight, open-source application that streamlines the prompt engineering process on Google Cloud with the help of Vertex AI SDKs. I found it interesting that this framework centralizes prompt engineering, making it easier for teams to create and refine AI prompts efficiently. How do you think structured frameworks like LLM-Evalkit can impact the future of AI development in your organization?

AWS outage takes down AI applications, many others: As enterprises rely more and more on generative AI models, AI applications have become just as susceptible to cloud outages as other business platforms.

When using AI to write a specification for your product please read it properly! The training set for GPT, Claude etc is heavily biased towards enterprise systems. As a result specifications tend to be over-engineered - especially for early stage products. I saw a spec the other day that required a High Availability cloud environment with automated failover - a hugely expensive infrastructure for a product that was going to have about 200 users! A good developer should challenge this (a bad one will say yes to everything and charge through the nose...) - but if you’re feeding your spec straight into Loveable/Replit you’re going to waste and awful lot of time and tokens doing things you just don’t need. If you’re not technically inclined this can be daunting - but if you’re aiming for a simple MVP and the spec is very long and full of jargon it’s time to review your prompts and double down on simplicity.

It's finally happening... This has been in the works for the better half of the year. We partnered with Yotta Data Services Private Limited to build India's first AI Cloud of its scale. Yotta's robust infrastructure is now powered by Simplismart MLOps orchestration layer. Running training jobs and inference workloads with lightning-fast speed is now as easy as it gets. It took a while, but we've finally built the most advanced AI platform on India's most reliable AI infrastructure 🚀 Simplismart Yotta Data Services Private Limited Amritanshu Jain Sunil Gupta Devansh Ghatak Ashin Uday Bhavesh Adhia

🚨 Partnership Launch 🚨 Simplismart is proud to partner with Yotta to launch Shakti Studio: An enterprise-grade AI Cloud platform designed to provide flexible fine-tuning and low-latency deployments across Generative AI models. Simplismart’s model-agnostic orchestration layer works with Yotta’s Sovereign infrastructure to provide the fastest inference on their L40S and H100 GPUs. → AI Endpoints (LLMs, TTS, ASR, Vision) you can consume instantly → Serverless GPUs (H100, L40S) available on-demand for fine-tuning and inference → Enterprise-ready security, observability and benchmarking Glad to partner with the team at Yotta Data Services Private Limited. (Link to the news article: https://lnkd.in/dEh3MYFT)

✈️ Landing AI the Right Way AI adoption isn’t just about experimenting with models — it’s about building a secure, scalable, and governed foundation that takes you from proof-of-concept ➝ production without turbulence. That’s exactly what the new Azure AI Landing Zone (AI ALZ) delivers: 🧩 Built on Cloud Adoption Framework (CAF) + Well-Architected Framework (WAF) 🔐 Security, compliance, and governance baked in from day one ⚡ Accelerated path from innovation to enterprise-scale deployment 🔄 Repeatable patterns for multiple AI use cases 🛠️ Powered by Azure AI Foundry for unified development, deployment, and observability Think of it as the blueprint for enterprise AI — where innovation meets governance, and scale meets responsibility. 📖 Read the full blog on the Microsoft Tech Community https://lnkd.in/erEWPtKb #Azure #AI #AzureAI #EnterpriseAI #CloudAdoption #WellArchitected #CloudComputing #AIAdoption #Architecture