Who should I follow to learn about LLMOps?

Follow a mix of **LLM systems engineers, MLOps practitioners, and production AI builders**: - Chip Huyen - excellent for ML systems design, deployment, inference, and production trade-offs. [Why MLOps Engineers Need to Think Like ML Researchers (And Here's Where to Start) 🧠](https://mlops.tv/p/why-mlops-engineers-need-to-think?utm_source=chatgpt.com) - Andrej Karpathy - best for understanding how LLMs work internally, from training to inference. [MLOps Roadmap for Seniors · MLOps MENA](https://mlopsmena.com/roadmaps/senior-mlops-engineer/?utm_source=chatgpt.com) - Simon Willison - practical LLM application engineering, tooling, and workflows. [Why MLOps Engineers Need to Think Like ML Researchers (And Here's Where to Start) 🧠](https://mlops.tv/p/why-mlops-engineers-need-to-think?utm_source=chatgpt.com) - Lilian Weng - deep technical explanations of architectures, agents, and AI research. [Why MLOps Engineers Need to Think Like ML Researchers (And Here's Where to Start) 🧠](https://mlops.tv/p/why-mlops-engineers-need-to-think?utm_source=chatgpt.com) - Hamel Husain - strong on evaluation, reliability, and building ML systems that work. [22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/?utm_source=chatgpt.com) - Josh Tobin - useful for ML observability and production ML thinking. [22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/?utm_source=chatgpt.com) - Ville Tuulos - valuable for ML infrastructure and orchestration patterns. [22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/?utm_source=chatgpt.com) - Demetrios Brinkmann - follow for community discussions, podcasts, and practitioner perspectives. [22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/?utm_source=chatgpt.com) Also follow communities: - MLOps Community - production ML/LLMOps talks and discussions. [LLMOps Courses & Certifications - Curated, Vendor-Neutral - LLMOps.si](https://llmops.si/learn/?utm_source=chatgpt.com) - Hugging Face - models, tooling, deployment patterns. [22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/?utm_source=chatgpt.com) For a practical LLMOps learning path: **Karpathy → Chip Huyen → Hamel Husain → MLOps Community → hands-on projects with evals, RAG, tracing, monitoring, and deployment.** [Why MLOps Engineers Need to Think Like ML Researchers (And Here's Where to Start) 🧠](https://mlops.tv/p/why-mlops-engineers-need-to-think?utm_source=chatgpt.com)

**Follow these experts to learn about LLMOps: Chip Huyen, Harrison Chase, Jason Wei, and Matei Zaharia. They combine hands-on engineering, open-source leadership, and deep research in deploying and scaling large language models.** ## 🌟 Key People to Follow in LLMOps Expert| Focus Area| Why Follow Them ---|---|--- **Chip Huyen**| ML systems design, LLMOps education| Author of _Designing Machine Learning Systems_, teaches practical deployment strategies, and shares deep insights on scaling LLMs.[22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/) **Harrison Chase**| LangChain & LLM application frameworks| Co-founder of LangChain, a leading framework for building LLM-powered apps. Essential voice on LLMOps tooling.[16 Notable AI LLM Engineers & Researchers (2026)](https://echoglobal.tech/technologies/ai-llm/) **Jason Wei**| Prompting & model evaluation| Researcher at OpenAI, pioneer in chain-of-thought prompting. His work shapes how LLMOps teams evaluate and improve models.[16 Notable AI LLM Engineers & Researchers (2026)](https://echoglobal.tech/technologies/ai-llm/) **Matei Zaharia**| MLflow, Databricks| Creator of Apache Spark and MLflow, driving open-source infrastructure for ML/LLMOps.[22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/) **Aparna Dhinakaran**| ML observability| Co-founder of Arize AI, focused on monitoring and debugging LLMs in production.[22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/) **Tim Dettmers**| Efficient LLM training| Known for research on training large models with limited resources.[16 Notable AI LLM Engineers & Researchers (2026)](https://echoglobal.tech/technologies/ai-llm/) **Alejandro Saucedo**| Open-source MLOps| Chair of the MLops Community, strong advocate for responsible AI deployment.[22 Notable MLOps Experts & Engineers (2026)](https://echoglobal.tech/technologies/mlops/) ## 🔧 Platforms & Communities to Watch * **LangSmith / LangChain** – tracing and debugging for LLM apps. * **Weights & Biases (W&B)** – experiment tracking and evaluation. * **Arize AI / Phoenix** – observability for LLM pipelines. * **MLflow** – open-source model registry and deployment.[Best LLMOps Platforms for Enterprise AI Teams: 2026 Guide](https://atlan.com/know/best-llmops-platforms/) These platforms are widely used by the experts above, so following their updates helps you stay aligned with best practices. ## ⚠️ Challenges & Risks in LLMOps * **Integration gaps**: Many LLM pilots fail not because of the model, but due to missing governance and monitoring.[17 Leading LLM Consulting Companies for 2026: Expert Guide](https://www.christianandtimbers.com/insights/17-leading-llm-consulting-companies-for-2026-expert-guide) * **Observability blind spots**: Without tools like Arize AI or LangSmith, teams struggle to debug failures in production. * **Rapidly evolving practices**: Techniques like retrieval-augmented generation (RAG) and prompt versioning are still maturing, so following active researchers ensures you don’t fall behind. ## ✅ Action Plan 1. **Follow experts on X (Twitter), LinkedIn, and GitHub** – most share code, papers, and practical insights. 2. **Engage with communities** like the MLOps Community (founded by Demetrios Brinkmann & Alejandro Saucedo). 3. **Experiment with tools** (LangChain, MLflow, W&B) to gain hands-on experience. 4. **Track consulting firms** specializing in LLMOps for enterprise deployment strategies.[17 Leading LLM Consulting Companies for 2026: Expert Guide](https://www.christianandtimbers.com/insights/17-leading-llm-consulting-companies-for-2026-expert-guide) Would you like me to curate a **Twitter/X follow list** of these experts so you can keep up with their latest posts in one place?

To master LLMOps (Large Language Model Operations), following practitioners, researchers, and organizations sharing practical production patterns, evaluation frameworks, and infrastructure scaling is essential. **Top Experts & Practitioners to Follow** * **Chip Huyen:** Co-founder of Claypot AI and author of _Designing Machine Learning Systems_. She writes extensively on the realities of production AI, LLM evaluation, and shifting paradigms from traditional MLOps. * **Hamel Husain:** An independent machine learning engineer and consultant who provides some of the most practical, hands-on guides for LLM evaluation, fine-tuning, and building reliable internal developer platforms. * **Eugene Yan:** Applied ML scientist at Amazon who writes deeply technical, highly regarded newsletters and case studies dissecting how top tier tech companies build, evaluate, and scale LLM applications. * **Jerry Liu:** Co-founder and CEO of LlamaIndex. Following him provides direct insights into data frameworks, advanced Retrieval-Augmented Generation (RAG) pipelines, and context-window optimization. **Organizations & Platforms to Follow** * **Weights & Biases (W&B):** Excellent for tracking experiments, prompt engineering versions, and visualization tools tailored specifically for LLM workflows. * **Hugging Face:** Essential for tracking open-source model deployment, serving tools like `vLLM`, and efficient model fine-tuning pipelines. * **TrueFoundry & Evidently AI:** Great companies and engineering blogs focused heavily on enterprise LLM orchestration, monitoring, cost governance, and catching data/model drift in production. Would you like a recommended reading list or a structured roadmap to get started with LLMOps tools?

To learn LLMOps (Large Language Model Operations), you should follow **top MLOps/LLM engineers, specialized educational platforms, and industry practitioners** who consistently publish production-grade architectures, cost-tracking strategies, and deployment guides. [I am learning LLMOps for High Paying Opportunities in 2026](https://www.youtube.com/watch?v=SArBu9ik524&t=617)[LLMOps Full Course 2026 | Zero to Hero](https://www.youtube.com/watch?v=vOBAPJ_4hzE) Watch this breakdown by cloud engineers to understand how LLMs work in real production systems: Key People and Creators to Follow * * * ** _Paul Iusztin_** \- Founder of [DecodingML](https://www.linkedin.com/posts/meri-nova_machinelearning-activity-7287914427822088192-guip), an expert educator who writes extensively on production-ready LLM systems, MLOps, and engineering best practices. * * * **_Abhishek Veeramalla_** \- A prominent cloud and DevOps educator sharing hands-on infrastructure-as-code and deployment pipelines suited for engineers transitioning into LLMOps. * * * **_Brij Kishore Pandey_** \- AI Architect focusing on RAG, agentic infrastructure, and scalable system designs. * * * **_Maria Vechtomova_** \- ML/LLMOps tech lead sharing enterprise-grade frameworks and architecture patterns. [Top AI and ML experts come from diverse backgrounds. | Meri](https://www.linkedin.com/posts/meri-nova_machinelearning-activity-7287914427822088192-guip)[Top LinkedIn Content on MLOps for AI Development](https://www.linkedin.com/top-content/artificial-intelligence/mlops-for-ai-development/) Essential Platforms and Organizations to Follow * * **[DeepLearning.AI](https://www.deeplearning.ai/courses/llmops)** \- Offers specialized, practical courses on managing LLM lifecycles, data pipelines, and evaluation metrics. * **[TrueFoundry Blog](https://www.truefoundry.com/blog/llmops-mastering-the-art-of-managing-large-language-models-challenges-best-practices-and-future-trends)** \- Regularly publishes technical deep-dives into vector databases, fine-tuning infrastructure, and cost optimization. * **Hugging Face / MLflow Communities** \- Follow their official engineering feeds for updates on open-source serving, model governance, and tracing guardrails. [LLMOps - DeepLearning.AI](https://www.deeplearning.ai/courses/llmops)[LLMOps Guide: Streamline Your Machine Learning Operations](https://www.truefoundry.com/blog/llmops-mastering-the-art-of-managing-large-language-models-challenges-best-practices-and-future-trends) * Would you like recommendations tailored to your background - such as focusing more on **DevOps/Infrastructure** or **Data Engineering/Fine-Tuning**?