UbiOps - powerful AI model serving & orchestration’s Post

Managing AI workloads on Kubernetes is becoming increasingly complex, and keeping things optimized can feel like a significant challenge. That’s where Spot by NetApp’s Ocean for AI steps in to simplify the process. Ocean for AI is designed to streamline resource optimization and scaling for AI inference tasks, allowing you to manage resources more efficiently and cost-effectively. This solution takes care of the operational complexities, giving you the freedom to focus on driving AI innovation. With Ocean handling the infrastructure, you can ensure your Kubernetes clusters operate smoothly, freeing you to concentrate on the strategic priorities that move your business forward. We understand the challenges that come with managing AI workloads. To explore how we can help streamline your operations, join our upcoming virtual session, "Streamline Kubernetes Infrastructure Management," on September 12th from 1:00 pm - 2:00 pm ET. Register today and let’s talk about achieving your goals: https://lnkd.in/ejgpdtuB #NetApp #Kubernetes #AI #CloudComputing #Innovation

Hear about our journey to AI/ML Ops with #Kubernetes, which is now the management and deployment platform for all OCI #GenerativeAI services.

Imagine a future where AI agents not only execute tasks but also reason, adapt, and decide (a mimic of these things, but usefully so) —taking us beyond directed automation into human/AI collaboration. The recent developments with Vertex AI Reasoning Engine and OneTwo on Google Cloud are steps in this direction. These tools are paving the way for AI agents that understand context, make decisions, and solve complex problems in ways that were once the sole domain of human intelligence. These type of advances in agentic workflows are approaching maturity (I think 6-12 months out). At that point, we could see them transforming how industries operate, with AI becoming a partner in complex work processes rather than just an enabler. The possibilities are huge, and as these technologies evolve, they will redefine what AI can achieve, opening up new services for businesses and how we get work done. The future is not yet here, but it’s coming fast—and it will be extraordinary. https://lnkd.in/gJADvjhJ

Artificial intelligence (#AI) deployments are exploding, but few organizations are positioned for success. Explore the promises and pitfalls revealed by F5's 2024 research: http://ms.spr.ly/6046lBka4 #f5 #NGINX #AI #DevOps

Kubernetes benefits for AI inference workloads are substantial but may come with operational complexities. Spot Ocean provides a comprehensive, scalable, and efficient infrastructure optimization and cost reduction solution for organizations looking to maximize their AI infrastructure budget. #spotbynetapp #kubernetes #ai #cloudoptimization #finops https://lnkd.in/e8aYevK3

4 key devsecops skills for the generative AI era

AI Gateways vs. API Gateways: What's the difference? 🤷 It's critical to understand their unique roles to properly design #AI infrastructure that can handle the requirements of modern applications. Learn more from our partner, F5. https://lnkd.in/g8CH9gyR

🚀 Embrace the AI Revolution: Explore cutting-edge solutions for customer service in the transformed tech landscape. 🌐 Dive into the Microsoft Ecosystem: Discover a range of tools—from low-code wonders to robust code-heavy options—tailored for diverse scenarios. Stay tuned for Part 1 on LinkedIn! 📌 #AI #CustomerService #TechInnovation #virtulpetals #azure #azuredevopsengineer #ai #microsoft365copilot #virtualpetals #techupdates

🚀 Exploring the Intersection of AI Governance and MLOps 🚀 As our reliance on AI and machine learning continues to grow, the need for robust AI governance becomes more critical. At Celestial Systems Inc., we've been delving deep into how governance frameworks intersect with MLOps to create ethical, efficient, and scalable AI solutions. In our latest blog post, we discuss the importance of establishing clear guidelines and processes to ensure transparency, accountability, and fairness in AI systems. From data privacy to bias mitigation, the fusion of AI governance with MLOps isn't just about managing models—it's about building trust with users and stakeholders. Here's my take: As we develop more advanced AI technologies, we must prioritize ethical considerations and regulatory compliance. By integrating governance into the MLOps lifecycle, we can better manage risks and create AI solutions that are not only innovative but also responsible and sustainable. Check out the full blog post and let me know your thoughts! How is your organization tackling AI governance? #AIGovernance #MLOps #EthicalAI #DataScience #MachineLearning #CelestialSystems

A Brilliant post from Mark Schwartz If you’re serious about using generative AI to meet a known and important business objective, think of it as functionality that js on a path to production and value creation. Proofs of concept are an important way to manage risks and validate your business case—not your case for the technology itself but for the business functionality you create with it. AWS has done the heavy lifting to help you mitigate risks: it is architected to be the world’s most secure and reliable infrastructure. The risk management framework you use for your other IT systems carries forward into the new generative AI applications you deploy. The path to production is open.