Oracle and Google Cloud expand partnership, offer Gemini 2.5 via OCI

The demand for deploying AI Agents and LLM-powered applications on the cloud is skyrocketing. Every company is racing to set up the right infrastructure to build, deploy, and scale their intelligent systems efficiently. If you’re building on AWS, one of the most powerful ways to bring your AI ideas to life is through Amazon Bedrock — a fully managed service designed to help you build, scale, maintain, and deploy foundation models and AI agent applications seamlessly. In my latest post, I break down everything you need to know to get started with AWS Bedrock — from selecting the right foundation model to integrating it into your architecture and scaling it for production. Whether you’re just exploring LLM infrastructure or preparing to deploy your first AI agent, this guide will give you the clarity and confidence to move fast while staying scalable. View post at --> https://lnkd.in/egySAYjT #AI #AWS #Bedrock #LLM #CloudComputing #AIAgents #MachineLearning

In part two of our Leadership Perspective with Ben Schreiner of Amazon Web Services (AWS), we continue the conversation on AI business transformation essentials and look ahead to AI’s long-term impact and its role in the years to come. Read it here: https://lnkd.in/dWidjktw #EnterpriseAI #AgenticAI #AIFuture #AIInnovation #ModernDataStrategy #BenSchreiner #AWS #StayinFront #OnDeviceAI #RetailOptimization #ConsumerGoodsTech #AITransformation

Data and AI professionals are losing the art of developing true MVPs. Here's how I avoid analysis paralysis and over-engineering to deliver. Let me introduce you to the “3-Resource Rule,” a minimalist and repeatable blueprint for creating AI MVPs. To build a powerful, secure, and low-cost AI demo, you need 3 things: 1. A laptop: A place to run code. 2. A Storage Account: A place to store data. 3. An AI Service: A way to access AI services via API. In my latest Substack article, I break down the 3-Resource Rule architecture that takes you from an idea to impactful demo, quickly. Read the article here: https://lnkd.in/etUhkRRz p.s. I mostly work in Azure but this pattern can be used in other cloud providers or even local development. The concept stays the same.

Everyone wants to talk about AI, but few talk about the work that makes it valuable💯 The truth is, AI is only as smart as the data behind it. You have to train your data before AI can truly help grow your business. At Red Oak Strategic we help clients do exactly that, building Amazon Web Services (AWS) solutions that organize, structure, and prepare data so AI can deliver real insights and solve real problems. Because when your data is ready, your AI becomes a trusted partner in driving smarter decisions. 💡 Start with your data. That’s where intelligence begins. Our Head of Engineering, Tyler Sanders, explains more: #AWSPartner #DataAnalytics #datatransformation #cloud #data #ai

🚀 Google Cloud: Leading the AI Revolution in 2025 Google Cloud is transforming how enterprises harness AI with its latest innovations. Powered by the Gemini AI suite, Google Cloud now offers agentic AI platforms that automate complex workflows in data engineering, data science, and analytics. The new Gemini Data Agents APIs enable seamless integration of AI into existing apps, while enhanced visual search and conversational analytics redefine accessibility. With distributed multimodal AI models and deep integration across Google Workspace, the platform boosts productivity and innovation at scale. How is Google Cloud powering your AI transformation journey? #BooleanSystems #GoogleCloud #GeminiAI #AgenticAI #DataCloud #AIInnovation #TechTrends2025 www.booleansystems.com

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

Are public cloud solutions the key to AI success, or are enterprises missing a crucial piece of the puzzle? Despite the scalability and cost efficiency of platforms like AWS, Microsoft, and Google, a staggering 80% of AI and machine learning workloads still face deployment challenges. The disconnect often lies in the ineffective integration of these tools with existing data ecosystems. To truly unlock AI's potential, businesses must prioritize connecting internal and external data sets, focusing on targeted use cases that bring tangible value. Investing in AI without a clear data strategy can lead to costly failures. #AI #MachineLearning #CloudInfrastructure #DataIntegration #PublicCloud

Amazon Web Services (AWS) has added Claude 4.5 Haiku, Anthropic’s new lightweight model, to Amazon Bedrock. The model offers near-frontier performance at a fraction of the cost, delivering faster response times, vision support, and scalability for latency-sensitive workflows and large-scale deployments. Discover how this model could change the economics of AI in your organisation: https://lnkd.in/dNPKnitc #AWS #AI #CloudComputing #EnterpriseAI

You don’t need a rocket ship to go to the grocery store. But that’s the approach so many business leaders take with GenAI. They assume that the bigger the model, the better the results. But that couldn’t be less true. I’ve seen businesses pour time and money into massive GenAI models, or what @EricMiller calls “super large language models” in this clip, only to discover that the ROI doesn’t justify the cost. They pay for more data storage and processing power than they could ever need for their business case, and end up using about 10% of the model they paid for. When you’re talking GenAI, bigger isn’t better. Relevant is better.  The key is aligning the right-sized technology to the right business challenge. That’s where projects start creating measurable value instead of runaway expenses. I had the pleasure of talking about this with our CTO Eric Miller and Bob Lindquist from @AWS in a recent webinar about moving GenAI from POC to production.  Highly recommend watching for anyone thinking about taking the next steps toward GenAI on AWS.  You can catch the full conversation here 👉 https://lnkd.in/gkaShiad #GenAI #AWS #AI #LLM #Cloud

Paths to ML Deployment on Google Cloud Huge thanks to Chansung Park and Sayak Paul, for creating this excellent visualization of Google Cloud's ML deployment options. Cloud Deployment Path (Top): Dataset → Vertex AI AutoML Training → Model Deployment → Vertex AI Endpoint Perfect for scalable cloud-based inference with full Vertex AI infrastructure support. Edge/Mobile Deployment Path (Bottom): Dataset → Vertex AI AutoML Edge Training → Firebase ML TFLite Export → Mobile Device Integration This is ideal for on-device inference, reduced latency, and offline capabilities. What I love about this comparison is how it shows that Google Cloud supports both enterprise-scale cloud deployments AND lightweight edge computing—all starting from the same dataset foundation. Whether you're building cloud-native AI services or embedded ML for mobile apps, Watchdog IT has you covered. #MachineLearning #GoogleCloud #VertexAI #MLOps #EdgeML #CloudComputing #AI