Enterprise AI Infrastructure 101: From Proof of Concept to Production скачать в хорошем качестве

Enterprise AI Infrastructure 101: From Proof of Concept to Production

Why do 87% of AI projects never make it to production? (It's not what you think...) In this inaugural Tampa Bay Enterprise AI Community meetup, we tackle the biggest challenge in enterprise AI: getting from a working proof of concept to a production system that actually serves users at scale. Most AI projects don't fail because the models don't work—they fail because of infrastructure complexity, compliance requirements, cost unpredictability, and planning failures that happen BEFORE you write a single line of code. WHO THIS IS FOR: • CTOs and Engineering Leaders planning AI initiatives • ML Engineers building production AI systems • Data Scientists moving models from laptop to production • Platform Engineers architecting AI infrastructure • Anyone wondering why their AI POC never reached production WHAT YOU'LL LEARN: PART 1: Strategic Overview • Why 87% of AI projects fail to reach production • The "impossible choice" between cloud and on-premises • POC vs Production requirements gap • Real-world cost analysis: $5K/month → $200K/month • Infrastructure patterns that actually work • Decision frameworks for hybrid architectures PART 2: Technical Deep-Dive • Why Kubernetes for AI infrastructure • GPU vs CPU resource allocation strategies • GPU scheduling and Multi-Instance GPU (MIG) • 50-70% cost reduction through GPU pooling • End-to-end ML pipelines (data prep → training → deployment) • KServe model deployment walkthrough • Multi-tenancy patterns and cost allocation • Security and noisy neighbor prevention PART 3: Q&A • Community questions on architecture, costs, compliance, and getting started KEY TAKEAWAYS: ✅ Enterprise AI is hard technically—but solvable with proven patterns ✅ Most failures are non-technical: governance, compliance, cost planning ✅ There is no perfect architecture—only trade-offs you choose deliberately ✅ Start small, iterate, scale: First production model in 6 months, not perfect platform in 18 months VERIFIED DATA & SOURCES: All statistics and technical claims are backed by: • MIT Media Lab (2025): 95% of GenAI pilots fail • VentureBeat, Gartner, Capgemini research • NVIDIA technical documentation and case studies • Production deployments at AWS, Uber, Snap • Real cloud GPU pricing (September 2025) Full references available in presentation slides. RESOURCES: 📝 Blog Post: "The Enterprise AI Infrastructure Stack: From POC to Production" 📄 Presentation Slides: [Link in comments] 💬 Community Slack: join.slack.com/t/enterpriseaicommunity/shared_invite/zt-3fhj8evxf-q3pXrl_epEkQBTLQgEciLA 📅 Meetup Group: meetup.com/enterprise-ai-community ABOUT THE PRESENTER: David Lapsley, Ph.D. (Engineering - Networking) • CTO, ActualyzeAI • Former AWS Director, Network Fabric Controllers (100+ datacenters, AI workloads at massive scale) • Former Cisco Director, DNA Center Maglev Platform (Kubernetes, $1B annual run rate) • 25+ years building networking & infrastructure platforms at scale ABOUT TAMPA BAY ENTERPRISE AI COMMUNITY: A practitioner-focused community for technical leaders and engineers building production AI systems. We share real-world patterns, battle-tested architectures, and hard-won lessons from deploying enterprise AI at scale. No vendor pitches. No theory. Just practitioners helping practitioners. SUBSCRIBE for monthly deep-dives on: • Production AI infrastructure patterns • Kubernetes and GPU orchestration • Cost optimization strategies • Compliance and governance • Hands-on workshops and tutorials JOIN THE CONVERSATION: • What AI infrastructure challenges are you facing? • What topics should we cover next? • Share your experiences in the comments! #EnterpriseAI #MachineLearning #Kubernetes #MLOps #AIInfrastructure #GPU #ModelServing #DataScience #ArtificialIntelligence #ProductionML #KServe #CloudComputing #DevOps #TechLeadership