Shiliyang (Bryce) Xu

If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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Observability tools keep getting louder. We made one that gets smarter. Introducing Mezmo’s AI-powered SRE agent for RCA, designed to eliminate noise, find real causality, and deliver reliable guidance—without lengthy onboarding or risky model training. This is AI observability built for humans and agents. Read the full breakdown →https://lnkd.in/g4AZKc9q

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What does it take to succeed in data science? Strong coding and AI/ML knowledge are essential — but they’re just the beginning. Check out my talk at the next MLOps Denver meetup where I'll share five key lessons from over eight years in the field. From advocating for yourself to building cross-functional collaboration, this talk goes beyond the technical to explore what truly drives long-term success in a technical field. Hope to see you there! #DataScience #CareerGrowth #AI #MachineLearning #WomenInTech #ProfessionalDevelopment

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Demos are optimized for applause; production is optimized for constraints. If you’re hiring GenAI engineers, you need proof signals like eval harnesses, SLO thinking, monitoring, and incident ownership. This playbook breaks down the production-grade skill stack, a 4-stage interview loop, take-home exercises, and an enterprise-friendly rubric. #GenerativeAI #EngineeringLeadership #Hiring #MLOps #AITrust 🔗 https://lnkd.in/g5MXTZRu

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Another great new educational series is starting at San Diego Machine Learning. With the explosion of AI, many people have been asking how to jump start their ML skills. SDML is responding with a weekly program based on the Hands-On Machine Learning book by Aurelien Geron. This free series will be available both in person and online on Saturdays, starting August 2nd. There are no prerequisites, and people who complete the program will learn all of the core ML skills needed to excel. https://lnkd.in/gB_3N2S4 #ai #ml #datascience #machinelearning

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Palantir AIP demonstrates what it truly means to operationalize AI at enterprise scale starting with trusted data, structured through Foundry Ontology, powered by AIP Core, and executed through intelligent agents with human oversight. This end-to-end flow Data → Ontology → AIP Core → Agents → Actions → Feedback enables secure, auditable, and continuously improving AI that moves beyond POCs into real business impact. Palantir AIP: https://lnkd.in/edcuDSeG

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Orthogonal ETL pipelines triangulate to truth. Most data quality frameworks are outdated. Coverage. Timeliness. Accuracy. Schema compliance. These metrics are artifacts of the 2010s data stack. In the age of agentic ETL, they are increasingly cheap. Our agents can scrape more sources, normalize schemas, and expand coverage at scale. But none of that guarantees truth. The most reliable datasets emerge from orthogonal pipelines reconstructing the same dataset independently. This principle already exists in modern statistics and AI: ensembles, mixture-of-experts, multimodal models, and orthogonal estimators. The insight is the same. Single pipelines produce data. Orthogonal systems converge on truth.

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No A/B test? No problem. Learn how Regression Discontinuity Design (RDD) can uncover causal effects in observational data by exploiting sharp cutoffs. Dive into the theory and a real-world e-commerce example in Alejandro Alvarez Pérez's newest article.

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We’re proud to partner with Groq to power enterprise AI agents with low-latency inference for performance-critical workloads. Teams on StackAI deploy Groq for latency-sensitive workflows: high-volume document analysis, customer-facing interactions, and internal knowledge retrieval. In each of these situations, fast, reliable responses materially improve user experience and productivity. StackAI and Groq are a prime example of how matching the right inference provider to the right workload helps enterprises build AI agents that perform in real production environments. Want to learn more? Read the full piece at the link in comments.  #StackAI #Groq #EnterpriseAI #AIagents

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I’ve been leaning on Claude Code a lot lately (I especially like the new Claude Opus 4.1) and this new short course from DeepLearning.AI crystalized a bunch of “do this, not that” habits for agentic coding. Claude Code: A Highly Agentic Coding Assistant (built with Anthropic) is clear, hands-on, and only 90 minutes or so long.  https://lnkd.in/guvsDnBg My big takeaway: Claude Code shines when you give it context + constraints and let it work in small, testable steps.  First reading the repo, then proposing a plan, then shipping diffs (not walls of code). The course walks through exactly that across three concrete projects (RAG chatbot, refactoring a notebook into a dashboard, and a Figma-to-web app build), plus GitHub hooks. If you are not already doing it, here were a few tips that can level up your usage: Use CLAUDE.md at the repo root (and even per-package in a monorepo) so Claude automatically pulls project norms, commands, and “gotchas” into context. It’s like a living, machine-readable onboarding doc. Create custom /slash commands by dropping prompt templates into .claude/commands.  This is great for repeatable workflows like “fix PR comments and push” or “triage Issue #1234.” Team-shareable via git. Headless mode for CI: run Claude Code non-interactively to label issues, do subjective linting (naming, clarity), or fan out codebase migrations. It’s an automation surface, not just a chat. MCP servers as power-ups: wire in tools (e.g., Puppeteer, Sentry) via .mcp.json so everyone on the repo gets the same agentic capabilities out of the box. Git + GitHub ergonomics: install gh and let Claude draft commit messages, open PRs, or resolve rebases—surprisingly effective when scoped to small diffs. Why I liked the course: it doesn’t just demo “AI writes code.” It teaches the workflow: have Claude summarize the codebase, set success criteria, ship incremental changes, and keep tests close. The GitHub hooks + notebook-to-dashboard refactor were especially practical. If you’re experimenting with agentic coding or trying to make it stick across a team, this is a crisp starting point. #AI #ClaudeCode #AgenticCoding #DeveloperExperience #MLOps #Productivity

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