Autonomous AI Lakehouse Combines Apache Iceberg and Oracle AI

🔍 Observability is becoming more important than the model itself In modern AI-powered systems, accuracy alone isn’t enough. If you can’t observe, trace, and explain what your system is doing in production, you don’t really control it. While building distributed AI & microservice platforms, I’ve seen this pattern repeatedly: 🚨 Systems don’t fail loudly, they degrade silently What made the difference in production: ✅ OpenTelemetry-first instrumentation across FastAPI services and LLM pipelines ✅ End-to-end tracing from API request → vector search → LLM inference ✅ Grafana dashboards for latency, token usage, retrieval quality, and error rates ✅ Drift detection signals surfaced early through logs and metrics ✅ Clear audit trails for compliance-heavy environments (SOC2, HIPAA, PCI) Observability isn’t “nice to have” anymore especially with: • LLM hallucinations • Model performance drift • Multi-agent workflows • Event-driven architectures The teams that win are the ones who can debug production AI with the same confidence as backend systems. If you’re building AI systems that need to scale and stay reliable, observability is where the real engineering happens. #Observability #OpenTelemetry #AIInfrastructure #LLMOps #BackendEngineering #FastAPI #Microservices #AWS #SoftwareEngineering

AI inference, where trained models generate outputs from new data, is expected to be the breakout focus in 2026, driving demand for custom accelerators and boosting data center server and storage revenues significantly.

You don’t need a data center to run a spreadsheet. Yet many teams use massive LLMs for tasks that don’t require massive intelligence. Using a 70B+ model to summarize documents, route tickets, or classify data isn’t innovation. It’s inefficiency. You burn compute, add latency, and inflate API costs for capability you don’t need. This is where SLMs (Small Language Models) come in. The shift isn’t about bigger models. It’s about architectural precision. Large models are powerful generalists. SLMs are task-specific, quantized, and deliver most of the utility at a fraction of the cost. Why this matters for 2026 budgets: • local or private deployment • faster, domain-aware tokenization • multiple SLMs outperform one bloated model Don’t build for hype. Architect for the task. [slm llm ai architecture inference efficiency system design] #AIArchitecture #EnterpriseAI #SLMs #SystemDesign #AIInfrastructure #Gisax

LLMs are only as reliable as the context they’re given. Without access to verified, structured financial intelligence, even advanced agents can produce outputs that are hard to validate or trust. That’s why connecting workflows to Bigdata.com via the Model Context Protocol (MCP) matters. MCP enables secure access to curated financial news, filings, transcripts, and citation-ready metadata - without custom integrations or brittle APIs. The result: grounded outputs, fewer hallucinations, and faster paths to production. Start the year with AI infrastructure built for accuracy, scale, and trust. https://lnkd.in/evxHkN3W

Hut 8's new AI data center deal with Fluid Stack could be a game-changer. The $7 billion, 15-year lease for 245 megawatts of AI hosting capacity is backed by Google, potentially reaching $17.7 billion. This positions Hut 8 for significant growth in AI model training and inference. Keep an eye on how this deal unfolds—it might just be the start of something much bigger. #AIDataCenter #Hut8 #FluidStack #GoogleAI #AIHosting

The AI bottleneck isn't models. It's infrastructure. Every enterprise is hitting the same wall: legacy databases that fragment data across silos, choke on real-time workloads, and force tradeoffs between speed and flexibility. SynergyDB was built for what's next. One unified engine supporting relational, document, graph, vector, and key-value paradigms. No more stitching together five different systems. No more choosing between OLTP and OLAP. No more compromises. AI needs data that moves at the speed of inference. Now it can. #SynergyDB #DataInfrastructure #EnterpriseAI #DatabaseEngineering

AI scales on abundance. Power, infrastructure, and scale. ⚡️ HIVE spent years building where those inputs were cheapest and cleanest. 🐝 Now those same sites are unlocking Tier III+ AI compute for the next wave of intelligence. 🧠

LLM performance issues are rarely caused by the model itself. They usually stem from how the surrounding system is designed. Without caching, every user query directly hits the LLM, resulting in: • Higher latency • Increased costs • Unnecessary compute usage With a proper cache layer: • Repeated queries are served instantly • Only new or unseen queries reach the LLM • Cost and response time are significantly reduced In production AI systems, caching is not just an optimization. It’s a fundamental architectural requirement. Scalable AI is built with backend thinking, not just better prompts. #LLMOps #AIEngineering #BackendEngineering #SystemDesign #ProductionAI #ScalableSystems #TechArchitecture

Most AI workloads don’t need a data center. They need: - Low latency - Predictable cost - Offline resilience - Local context The missing piece isn’t another model. It’s execution. Mirai exists to make local and hybrid inference a first-class option. Learn more: https://trymirai.com/

Avoid relying on a single “general-purpose” model for every task. Instead, introduce a Agent #Router 🚦 In modern AI architectures, not every request requires a large, high-cost model such as #GPT-4o or #Claude 3.5 Sonnet. A Router Agent acts as an intelligent gateway, analyzing each request and routing it to the most appropriate model or tool based on complexity, latency requirements, and cost. 📍 Practical example (An Assistant): •  “What is my current balance?” → Routed to a SQL or database query tool •  “Summarize this 100-page report.” → Routed to a long-context large language model •  “Say hello.” → Routed to a lightweight, low-cost model (e.g., LLaMA 3 8B) This approach significantly reduces response time and optimizes infrastructure spend while maintaining high-quality outputs. #Efficiency #AIOps #SoftwareArchitecture #AI #Datascience