Beyond the Big Three: Building Smarter AI Workloads with Neoclouds
Aaron Wise
Neoclouds offer AI-optimized infrastructure that complements hyperscalers for specific workloads like training and inference.
Category
Data Science · Page 2
Articles
From Prototype to Production: Design Patterns That Make AI Actually Work
Aaron Wise
Scalable AI systems depend on repeatable patterns for data, deployment, and retraining as much as model performance.
Stateless by Design: Why Smart Systems Don’t Remember Everything
Aaron Wise
Zero data retention is an architectural and ethical choice to process user data without long-term storage, reducing risk and improving trust.
The Right Tool for the Write Job: Databases, Caches, and When to Use What
Aaron Wise
Match data shape and access patterns to the right store: relational for consistency, vector for similarity search, and caches for speed.
Trust, But Verify: Hardening Your MCP Server from the Socket Up
Aaron Wise
Secure MCP servers require zero-trust design, mutual TLS, schema validation, and runtime policy enforcement to prevent context-based attacks.
Terms of (Algorithmic) Endearment: Engineering Trust into Your AI Stack
Aaron Wise
Engineering trust into your AI stack demands ethics embedded in product design through bias audits, explainability tools, and cross-functional oversight.
The Agent Assembly Line: Orchestrating AI Systems with MCP, A2A, AG-UI, and RAG
Aaron Wise
MCP enables modular AI integration, A2A secures agent communication, AG-UI shifts user interaction, and RAG enhances LLM relevance with retrieval.
Data on Tap: Engineering Modern Pipelines from Batch to Real-Time
Aaron Wise
Modern data stacks prioritize activation over storage, using modular tools plus observability, lineage and quality checks to make data trustworthy.
The World Isn’t Flat—Why Your Data Pipeline Shouldn’t Be Either
Aaron Wise
3D data adds depth to pipelines by modeling the world in space, time, and context, requiring richer structures and new engineering patterns.
Talk Smart to Me: How Agent2Agent Protocol Powers Autonomous Collaboration
Aaron Wise
Agent2Agent (A2A) enables secure, semantical peer-to-peer communication for autonomous AI agents.
The Trust Stack: Building AI You Can Rely On from Data to Deployment
Aaron Wise
Trust in AI requires a layered stack from data transparency to human oversight, ensuring reliability, fairness, and accountability at every stage.
Open Tables, Open Futures: Building Scalable, Interoperable Data Platforms
Aaron Wise
Open table formats add ACID transactions and schema evolution to data lakes, enabling multi-engine compatibility and avoiding vendor lock-in.
Scaling Wisdom: Designing Human-in-the-Loop Architectures for Intelligent Agents
Aaron Wise
Human-in-the-loop agentic AI combines autonomous decision-making with strategic human oversight to balance speed, control, and accountability.
Data Nationalism and the AI Hunger Games: When Foundation Models Run Out of Fuel
Aaron Wise
Data scarcity, legal limits, and global divides are creating a foundation model training crisis, pushing leaders to prioritize data diversity and ethics.
Automate the Boring, Govern the Risky: The Dual Mandate of Modern Data Teams
Aaron Wise
Data teams must automate routine workflows while actively governing high-risk AI and data practices to ensure both speed and trust.
From Swamps to Stacks: Operational Excellence in the Modern Data Engineering Landscape
Aaron Wise
Metadata-driven stacks and DataOps practices turn fragmented data workflows into reliable, scalable infrastructure.
Remember Me? Architecting Memory with Model Context Protocol Servers
Aaron Wise
MCP servers provide structured memory management for LLMs, enabling persistent context across sessions and tasks.
LLM Ops: Turning Prompt Chaos into a Fine-Tuned Machine
Aaron Wise
Prompt engineering evolves into a pipeline with templating, version control, and feedback loops to replace ad hoc hacks.
From Models to Microservices: How Great Data and Engineering Teams Lead Through Thought
Aaron Wise
Thought leadership from technical teams is a strategic lever that builds influence, attracts talent, and sets industry standards.
Pipelines and Predictions: Making AI a First-Class Citizen in Your SDLC
Aaron Wise
Integrating AI/ML into the SDLC requires treating models as production code, with shared ownership, CI/CD pipelines, and cross-functional teams.
Prompt and Circumstance: The Art and Science of Teaching Large Language Models
Aaron Wise
Few-shot prompting lets teams guide AI with examples, not retraining, by shaping clear tasks and patterns in real time.
Linking It All Together: The Power of Graph Databases and Knowledge Graphs
Aaron Wise
Graph databases shine when data relationships are as vital as the data, enabling efficient modeling of complex, interconnected systems.
Gut Feel vs. Cold Facts: When to Trust Soft or Hard Data
Aaron Wise
Balance soft and hard data by using hard data for precision and soft data for human context to make complete decisions.
Decentralized Intelligence: Smarter Models, Safer Data, Zero Trust Issues
Aaron Wise
Federated learning trains AI models across decentralized data sources while preserving privacy by sharing only model updates, not raw data.