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What does your model evaluation pipeline actually look like in production?
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
Community
Ask, compare and learn from people who run this stuff.
This is where people who run enterprise technology compare notes before a decision: what they’re evaluating, what they’d pick again and what went wrong. Most members are practitioners. Anyone who works for a vendor carries their company’s name on every post.
The bar is specifics over opinions: what you ran, at what scale and what you learned. Moderators check each new member’s first posts.
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Threads
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How are you handling semantic layers and metric definitions across BI tools?
dbt semantic layer, a headless BI layer, or definitions living in each dashboard tool? What has reduced the “two numbers for revenue” problem for you?
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What is your approach to zero-downtime schema migrations at scale?
Expand-and-contract, online schema change tools, feature flags? Share what has kept large-table migrations boring.
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What does your generative AI cost per useful answer look like?
Token spend, retrieval cost, caching, model routing. How are you measuring and reducing cost without hurting quality?
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Which skills are you hiring for in FY27 that you were not a year ago?
AI engineering, platform, FinOps, security automation? Share what is on your hiring plan.
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What is the one cloud architecture decision you would undo if you could?
Account structure, networking, region choice, managed-service lock-in: the lessons that cost the most are the most useful to others.
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Have AI coding assistants changed how you review code?
More PRs, bigger PRs, new review checklists? Share what changed in your process and quality metrics.
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How are you securing employee use of generative AI tools?
Blocking, allow-listing, DLP, enterprise licences, policy and training: what combination is working in practice?
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How many Kubernetes clusters do you run, and how do you keep upgrades sane?
Fleet size, managed vs self-managed, and the tooling that keeps version upgrades from becoming a quarterly fire drill.
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How are you preparing the network for AI workloads?
East-west bandwidth, GPU cluster fabrics, data movement between sites: what has changed in your network plans?
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Post-VMware licensing changes: what did you actually do?
Renegotiated, moved to another hypervisor, containerised, or went to public cloud? Share your path and what surprised you.
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What makes a vendor proof of concept worth your time?
Success criteria, timeboxes, data access, who is in the room. Share what separates useful PoCs from sales theatre.
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How are you approaching AI governance and model risk management?
Inventories, risk tiers, approval workflows, monitoring. What framework did you adopt and what does day-to-day governance look like?
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Which workflow did you automate with an AI agent first, and did it stick?
We are hearing a lot about agent pilots and far less about agents still running six months later. What was the first use case, what guardrails did you need, and is it still in production?
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What is in your internal developer platform, and what did developers actually adopt?
Golden paths, templates, portals, self-service environments. Which parts get used every day and which became shelfware?
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Which FinOps practice saved you the most money in the last year?
Commitment management, rightsizing, tagging and showback, architecture changes? Numbers welcome.
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What is your approach to managing thousands of edge devices?
Fleet management, updates, security patching and observability at the edge: what tools and practices have held up?
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Which security investments are you prioritising for FY27?
Identity, cloud security posture, AI security, SOC automation, resilience? Share your top two and why.
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How are you evaluating AI features in your ERP/CRM suites?
Vendors are bundling copilots into every suite. How are you deciding what to switch on, and how are you measuring value?
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How are you getting GPU capacity for AI in 2026?
Reserved cloud capacity, neoclouds, on-prem clusters, shared internal pools? What are lead times and utilisation like?
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How are you controlling observability costs without losing visibility?
Sampling, tiered retention, OpenTelemetry pipelines, switching vendors? What cut the bill the most?
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How far along is your zero-trust journey, honestly?
Identity-first access, device posture, microsegmentation: which parts are done and which keep slipping?
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What does your ransomware recovery plan look like after the last round of tests?
Immutable backups, isolated recovery environments, recovery-time testing. What did your last exercise reveal?
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How are you allocating AI and GPU costs back to teams?
Shared GPU pools and model APIs make showback harder. What allocation model is working?