Atlanta Cloud + AI Conference

Keynote

Miguel Wood

Enterprise Cloud Solutions Architect and AI/Data Expert

Your AI Strategy Is Built on a Lie — And Your Data Knows It

Every enterprise has an AI strategy. Almost none of them are starting in the right place. Organizations are racing to deploy large language models, copilots, and AI agents while avoiding the uncomfortable question underneath: is your data estate actually ready to support what you are asking these models to do?

The answer, for most organizations, is no. And the consequences are not hypothetical. Failed AI initiatives are already producing measurable losses — not because the models are inadequate, but because the data foundations they depend on were never assessed, governed, or architected for the workloads being deployed against them.

This keynote introduces the AI Readiness Framework — a three-phase methodology built from over two decades of enterprise deployments across government, Defense Industrial Base, and commercial sectors. The framework provides a structured, repeatable approach for organizations to validate their AI vision against business outcomes, confront the real condition of their data estate, and build governance architectures that scale across multi-model environments.

Miguel Wood — one of fewer than 100 Microsoft Certified Solutions Master charter holders worldwide, a U.S. Army veteran, and the CEO of an enterprise AI consulting firm — delivers this keynote as a field report from real engagements: a composite failure case where a seven-figure AI initiative collapsed because no one audited the data, and a composite success case where the same framework turned a stalled program into a governed, production-grade AI deployment. Both are drawn from direct practitioner experience.

Attendees will leave with a concrete methodology they can apply immediately, architectural patterns for building governance into multi-model environments, and the honest clarity that comes from understanding what has to happen before the first model ever gets deployed.

What is the Atlanta Cloud + AI Conference?

The Cloud + AI Conference is a community focused event by and for the cloud community.

The Atlanta Cloud + AI Conference draws upon the expertise of local and regional administrators, architects, and experts who come together to share their real-world experiences, lessons learned, best practices, and general knowledge with other interested individuals.

AI Applications & Intelligent Systems
- LLM-powered applications
- Retrieval Augmented Generation (RAG)
- AI copilots and developer assistants
- AI agents and multi-agent systems
- Prompt engineering strategies
- Tool-using AI systems
- AI application evaluation
- Guardrails and hallucination mitigation
AI Infrastructure & Model Operations
- Model deployment platforms
- LLM inference optimization
- GPU infrastructure and scheduling
- AI workload orchestration
- Model serving architectures
- AI observability
- Model performance monitoring
- Cost-efficient inference
Data Platforms for AI
- Vector databases and embedding pipelines
- Lakehouse architectures
- Streaming data platforms
- Feature stores
- Data quality and validation
- Metadata and lineage
- Data governance
Cloud Native Architecture
- Microservices architecture
- Event-driven systems
- API-first architecture
- Service mesh adoption
- Stateful cloud-native systems
- Workflow orchestration
- Distributed consistency patterns
- Internal developer platform architectures
- Golden paths and platform design patterns
Kubernetes & Workload Orchestration
- Kubernetes architecture
- Cluster scaling strategies
- Multi-cluster operations
- Operator patterns
- Scheduling and resource management
- GPU scheduling for AI workloads
- Platform networking
- Self-service Kubernetes platforms
Serverless & Event Platforms
- Function-as-a-Service architectures
- Event streaming systems
- Event-driven microservices
- Durable workflows
- Serverless AI inference
- Async processing architectures

Cloud Infrastructure & Infrastructure as Code
- Infrastructure as Code patterns
- GitOps workflows
- Immutable infrastructure
- Multi-cloud architectures
- Resource provisioning automation
- Cloud networking design
- Storage and compute optimization
- Self-service infrastructure platforms
Observability, Reliability & SRE
- Observability platforms
- OpenTelemetry pipelines
- Distributed tracing
- Service Level Objectives (SLOs)
- Incident management
- Chaos engineering
- Debugging production systems
- Developer productivity metrics
Security, Identity & AI Safety
- Zero trust architecture
- Identity and access management
- Secrets management
- Software supply chain security
- Runtime threat detection
- AI model security
- Prompt injection defenses
- AI governance and compliance
Multi-Cloud, Edge & Hybrid Systems
- Hybrid cloud architecture
- Edge computing platforms
- IoT platforms
- Multi-cloud orchestration
- Edge AI inference
- 5G-enabled distributed workloads
Professional Development & Soft Skills
- Career management and mentorship
- Team building and leadership
- Starting or running a tech consulting business
- Resume building and interview preparation
- Public speaking and presenting
- Effective communication for engineers
- Managing organizational change
- Leading cloud and AI transformations
Cloud & AI Foundations (Student / Beginner Track)
- Introduction to cloud computing
- Containers and Kubernetes basics
- Introduction to AI and machine learning
- Building your first cloud application
- Prompt engineering fundamentals
- DevOps basics and CI/CD
- Git and collaborative development
- Career paths in cloud and AI
- Building a technical portfolio