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LLMOps

24 articles · Page 1 of 1
Data Bricklaying Diary
Data Bricklaying Diary
Aug 29, 2026 · Operations

From LLMOps to AgentOps: Operating Enterprise Agents Across Full Task Lifecycles

This article argues that enterprises need AgentOps, not just LLMOps, to manage AI agents that execute multi-step tasks with tools, state, and human oversight, detailing six key capabilities: task identity, state checkpoints, component versioning, end-to-end observability, task-level evaluation, and human-in-the-loop as a first-class operational state.

AI agentsAgentOpsLLMOps
0 likes · 15 min read
From LLMOps to AgentOps: Operating Enterprise Agents Across Full Task Lifecycles
Tencent Cloud Developer
Tencent Cloud Developer
Aug 21, 2026 · Artificial Intelligence

12 Unfiltered Observations on Working with AI

The author, an experienced backend developer, shares twelve hard‑earned observations about integrating AI into daily coding, debugging, and review workflows, highlighting the importance of an AI‑first mindset, the shift from ability to willingness, prompt engineering, human judgment, and the broader impact on teams and knowledge management.

AIKnowledge ManagementLLMOps
0 likes · 11 min read
12 Unfiltered Observations on Working with AI
Data Bricklaying Diary
Data Bricklaying Diary
Jul 23, 2026 · Artificial Intelligence

Operating High-Quality Datasets as Continuous Data Products for AI

This article presents a six-step framework for operating high-quality datasets as continuous data products, covering responsibility assignment, version baselines, quality and AI effect monitoring, feedback-to-candidate pipelines, controlled release strategies, and retirement mechanisms to ensure datasets evolve with business, models, and risk boundaries.

AI data productsAgentOpsDataOps
0 likes · 14 min read
Operating High-Quality Datasets as Continuous Data Products for AI
ThinkingAgent
ThinkingAgent
Jul 22, 2026 · Artificial Intelligence

Final Chapter: Cutting‑Edge DevEx to Lower AI Development Barriers

The article presents a comprehensive analysis of AI developer experience (DevEx), detailing why traditional DevEx falls short for LLM‑driven applications, describing a seven‑component platform that spans Playground, Trace replay, Prompt diff, RAG debugging, Eval dashboards, SDK/CLI, and scaffolding templates, and offering concrete architecture, code, metric baselines, framework comparisons, and best‑practice recommendations for enterprise adoption.

AIDevExEval
0 likes · 30 min read
Final Chapter: Cutting‑Edge DevEx to Lower AI Development Barriers
dbaplus Community
dbaplus Community
May 14, 2026 · Big Data

Building a ‘One‑Sentence Bank’: Big Data and AI Fusion for Small Banks

The article outlines the evolution of big data in banking, compares management models for heterogeneous data, describes the shift from data engineering to knowledge engineering, introduces LLMOps for high‑quality knowledge bases, and details how integrating AI and data can enable a “one‑sentence bank” that answers queries and executes tasks.

Artificial IntelligenceKnowledge EngineeringLLMOps
0 likes · 22 min read
Building a ‘One‑Sentence Bank’: Big Data and AI Fusion for Small Banks
DeepNoMind
DeepNoMind
Apr 25, 2026 · Operations

Why DevOps Is Dying: 3 High‑Value Roles to Pursue in 2026

The article argues that AI agents are rapidly automating traditional DevOps tasks such as IaC, CI/CD pipeline maintenance, and incident triage, and outlines three emerging career paths—AI infrastructure/LLMOps engineering, platform engineering, and AI‑focused SRE—to stay relevant in 2026.

AI OpsGPU SchedulingInfrastructure as Code
0 likes · 7 min read
Why DevOps Is Dying: 3 High‑Value Roles to Pursue in 2026
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Mar 26, 2026 · Artificial Intelligence

How to Build a Full‑Stack RAG Chatbot Using LangChain, FAISS & Langfuse

This guide walks through an end‑to‑end RAG implementation with LangChain, covering multi‑format document loading, recursive text splitting, embedding selection, FAISS vector storage, ConversationalRetrievalChain setup, prompt engineering, source citation, Langfuse observability, and best‑practice configuration management.

FAISSLLMOpsLangChain
0 likes · 13 min read
How to Build a Full‑Stack RAG Chatbot Using LangChain, FAISS & Langfuse
Yunqi AI+
Yunqi AI+
Feb 13, 2026 · Artificial Intelligence

AI Engineering: Methodology and Practice for Turning Generative AI into Production Systems

The article outlines a comprehensive AI engineering methodology—including the TPMR framework, an AI‑driven development lifecycle, talent transformation from co‑pilot to AI pilot, and a practical enterprise adoption roadmap—to move generative AI and large models from experimental prototypes to production‑grade systems.

AI EngineeringAI lifecycleLLMOps
0 likes · 5 min read
AI Engineering: Methodology and Practice for Turning Generative AI into Production Systems
AI Tech Publishing
AI Tech Publishing
Nov 20, 2025 · Artificial Intelligence

Million‑Dollar AI Playbook: From Prompt Engineering to Agents – Anthropic’s Full PDF Unpacked

Anthropic’s enterprise guide shows how early adopters boost productivity—20‑35% faster customer service, 30‑50% higher content output, 15% less coding time—and outlines a four‑step framework, prompt‑engineering formula, and agent roadmap to turn AI into measurable business value.

AI implementationAgentsAnthropic
0 likes · 10 min read
Million‑Dollar AI Playbook: From Prompt Engineering to Agents – Anthropic’s Full PDF Unpacked
360 Smart Cloud
360 Smart Cloud
Nov 14, 2025 · Artificial Intelligence

How TLM Platform Powers LLM Ops with PPO, GRPO and Reinforcement Evaluators

The article introduces the TLM large‑model development platform, details its fine‑tuning options, explains reinforcement learning fundamentals and key algorithms such as PPO and the newer GRPO, describes the architecture of a reinforcement evaluator, and shows how to configure RL training on the platform.

AI platformGRPOLLMOps
0 likes · 10 min read
How TLM Platform Powers LLM Ops with PPO, GRPO and Reinforcement Evaluators
DevOps Cloud Academy
DevOps Cloud Academy
Sep 28, 2025 · Operations

Mastering LLMOps: Essential Practices for Managing Large Language Models

This article outlines the lifecycle of large language models and presents LLMOps best practices—including data management, model development, deployment, monitoring, prompt engineering, and security—to help engineers build, scale, and maintain production-ready LLM applications.

Artificial IntelligenceLLMOpsVector Databases
0 likes · 19 min read
Mastering LLMOps: Essential Practices for Managing Large Language Models
Efficient Ops
Efficient Ops
Jul 1, 2025 · Operations

Inside Lenovo CloudOps: AI‑Driven Ops, LLMOps & FinOps Insights

The Lenovo Smart Cloud CloudOps session at the 26th GOPS Global Operations Conference showcased five deep‑dive topics—including large‑model‑powered intelligent operations, enterprise LLMOps, FinOps‑driven cost governance, cross‑region distributed ops, and SAP global ops—offering practical pathways for enterprises to accelerate their intelligent transformation.

AI OpsCloud OperationsDistributed Operations
0 likes · 8 min read
Inside Lenovo CloudOps: AI‑Driven Ops, LLMOps & FinOps Insights
Go Programming World
Go Programming World
Apr 22, 2025 · Artificial Intelligence

Design and Implementation of an Enterprise‑Grade LLMOPS Platform (EasyAI)

This article presents a comprehensive overview of building an enterprise‑level LLMOPS platform—including concept definitions, the relationship between LLMOPS, MLOps and intelligent agent platforms, four development tiers, architecture layers, core technical concerns, deployment options, and the benefits of cloud‑native AI development.

AI platformDevOpsGo
0 likes · 15 min read
Design and Implementation of an Enterprise‑Grade LLMOPS Platform (EasyAI)
Efficient Ops
Efficient Ops
Mar 9, 2025 · Artificial Intelligence

Essential LLMOps Tools: Build, Deploy, Monitor, and Manage Large Language Models

LLMOps, the end-to-end methodology for managing large language models, encompasses a curated set of development, deployment, monitoring, and local management tools—such as LangChain, vLLM, LangSmith, and Ollama—enabling practitioners to efficiently build, scale, and maintain AI applications.

AI developmentLLMOpslarge language models
0 likes · 6 min read
Essential LLMOps Tools: Build, Deploy, Monitor, and Manage Large Language Models
JavaEdge
JavaEdge
Feb 2, 2025 · Artificial Intelligence

Mastering LLMOps: From Model Deployment to Scalable AI Operations

This article explains LLMOps—its goals, core activities, benefits, best practices, and how using an LLMOps platform like Dify can dramatically cut development time, simplify prompt engineering, data preparation, monitoring, and deployment of large language models.

AI operationsLLMOpsdata management
0 likes · 13 min read
Mastering LLMOps: From Model Deployment to Scalable AI Operations
DataFunSummit
DataFunSummit
Jan 31, 2025 · Artificial Intelligence

LLMOps: Building a Prompt‑Driven Engine for AI Operations

This article presents the concept of LLMOps—applying large language models to AIOps—by analyzing prompt challenges, introducing the LogPrompt engine for log analysis, describing a prompt‑learning data flywheel with CoachLM optimization, reporting experimental results, and outlining future multi‑modal directions.

AIOpsCoachLMData Flywheel
0 likes · 16 min read
LLMOps: Building a Prompt‑Driven Engine for AI Operations
DataFunSummit
DataFunSummit
Jan 11, 2025 · Artificial Intelligence

Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview

This article presents a detailed overview of generative AI lifecycle management, covering practical use cases such as email summarization, the roles of providers, fine‑tuners and consumers, MLOps/LLMOps processes, retrieval‑augmented generation, efficient fine‑tuning methods like PEFT, and Amazon Bedrock services for model deployment and monitoring.

Amazon BedrockLLMOpsMLOps
0 likes · 14 min read
Generative AI Applications, MLOps, and LLMOps: A Comprehensive Overview
Fighter's World
Fighter's World
Oct 26, 2024 · Artificial Intelligence

Key Considerations for Deploying Large Language Models in Cloud Services

The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.

AI DeploymentAlibaba CloudLLMOps
0 likes · 8 min read
Key Considerations for Deploying Large Language Models in Cloud Services
DataFunSummit
DataFunSummit
May 10, 2024 · Artificial Intelligence

LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions

This article introduces LLMOps by defining large language model operations, explains the three stages of LLM development, details modern fine‑tuning methods such as PEFT, Adapter, Prefix, Prompt and LoRA, outlines the architecture for building LLM applications, discusses the main difficulties of agent‑based deployments, and presents practical solutions including Prompt IDE, low‑code deployment, monitoring and cost control.

AI operationsFine-tuningLLMOps
0 likes · 14 min read
LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions
21CTO
21CTO
Apr 29, 2024 · Artificial Intelligence

Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack

This article explores how developers can integrate large language models into applications by comparing fine‑tuning and context learning, detailing each method’s advantages and drawbacks, and presenting a four‑layer LLM tech stack—data, model, orchestration, and operations—with practical tooling examples.

AI StackContext LearningFine-tuning
0 likes · 16 min read
Fine‑Tuning vs. Context Learning: Building Apps with the Emerging LLM Tech Stack