In the AI‑driven era of 2026, data professionals need to focus on five high‑impact capabilities—data governance, practical large‑model usage, MLOps, data storytelling, and AI compliance—to stay indispensable, with each skill backed by industry reports, job growth data, and concrete learning pathways.
Amid exploding large‑model deployments, AI teams struggle with slow test feedback, but AI‑native testing tools—through intelligent load modeling, inference‑layer root‑cause analysis, and self‑healing loops—demonstrate concrete latency reductions, resource savings, and faster issue remediation.
In 2026, model evaluation has become the pivotal bottleneck in AI engineering, with exploding compute, data‑compliance, and tooling costs forcing a shift from labor‑intensive testing to quantifiable business value, and three levers—dynamic granularity, synthetic data loops, and evaluation‑as‑a‑service—offering a path to a cost‑benefit inflection point.
The article explains that most AI project failures stem from unrealistic evaluation rather than model intelligence, and outlines concrete practices—business‑aligned metrics, scenario sandboxes, human‑in‑the‑loop reviews, and auditable documentation—to make model evaluation truly actionable.
The article examines why most companies are still at the proof‑of‑concept stage for AI‑enabled testing, outlines three practical pillars—data, scenario selection, and closed‑loop feedback—and warns of common pseudo‑AI pitfalls through concrete industry case studies.
A veteran programmer analyses how AI tools like Copilot and Claude boost development speed, reshape job structures, risk skill erosion, and force a shift toward AI‑augmented roles, while offering concrete data and practical advice for staying relevant over the next five years.
McKinsey’s report reveals that AI is moving from pilot projects to large‑scale deployment across Southeast Asia, driven by youthful, mobile‑first populations and massive cloud investments, yet talent shortages, integration complexity, and unclear ROI remain the biggest hurdles for enterprises.
With RAG becoming the backbone of enterprise AI, traditional API‑UI testing misses critical semantic errors, leading to high hallucination rates; this article outlines why conventional methods fail and presents a three‑pillar transformation—skill rebuilding, process reengineering, and advanced tooling—backed by real‑world case studies.
The article outlines a comprehensive, repeatable regression testing framework for TensorRT inference pipelines, covering engine build validation, functional correctness against golden outputs, performance monitoring, common pitfalls, and CI/CD integration to ensure model updates remain both fast and reliable.
The article examines why traditional deterministic testing fails for probabilistic LLMs and outlines a new testing paradigm that emphasizes safety, robustness, controllability, and explainability, illustrated with real‑world cases and a step‑by‑step MLOps workflow.
The article outlines a four‑layer framework—process, technology, risk, and culture—to help enterprises implement AI‑in‑the‑loop safely, ensuring AI assists decisions while humans retain final authority, with concrete governance, data, and organizational practices.
This guide explains what AI engineering entails, outlines the end‑to‑end workflow from problem definition and data preparation through model development, deployment, and monitoring, and highlights the essential programming, cloud, and MLOps skills, career tracks, emerging trends, and salary outlook for aspiring AI engineers.
This article explains what MLOps is, why a robust MLOps framework is essential for businesses, outlines its core components, compares MLOps with AIOps, details the benefits of investing in MLOps, and provides a step‑by‑step guide to designing enterprise‑grade AI MLOps infrastructure.
This guide explains what model deployment is, why it matters, the various deployment types, readiness criteria, best practices, common challenges, real‑world case studies, and the most popular tools and platforms for deploying machine learning models in production.
Argo Workflows, the top‑rated CNCF project, extends Kubernetes to orchestrate AI, ML, and data pipelines with a scalable, cloud‑native architecture, offering powerful scheduling, Python SDK support, and new plugins for Spark, Ray, and PyTorch.
This article explains why cost and performance are critical in the AI era, outlines the three main pain points of AI application development, and details a full‑stack observability solution—including architecture layers, key metrics like TTFT and TPOT, OpenTelemetry tracing, and practical tips for frameworks such as Dify—integrated into Alibaba Cloud CloudMonitor 2.0.
This article examines the evolving role of the AI engineer, contrasting it with AI researchers, ML engineers, and software engineers, outlines essential skills such as prompt engineering, MLOps, and data integration, and predicts how AI engineering will become a pivotal, high‑demand discipline in the coming years.
Learn how to quickly set up Alibaba Cloud’s Bailei platform to call the open-source Qwen3 large language model, explore its cost‑effective performance, dual‑mode reasoning, multilingual support, and enhanced agent capabilities, and follow step‑by‑step instructions for API key configuration, Cherry Studio integration, and tool‑calling setup.
Meng, a former Tencent and Alibaba engineer, led Dewu’s one‑stop large‑model training and inference platform, cutting integration costs, creating a shared GPU pool and CI/CD pipeline, building a Milvus vector‑database, and driving self‑directed learning that boosted business value, user experience, and set a roadmap for future RAG and cloud‑native optimizations.
The article analyzes the emerging divide in the AI ecosystem where a few giants focus on resource‑intensive large‑model research and training while most companies shift to leveraging existing models for application development, outlining the implications, challenges, and strategic advice for developers and enterprises.
This article describes how Tubi improved the latency of its Redis‑backed online feature store for machine‑learning inference by analyzing query patterns, measuring client‑side bottlenecks, and applying optimizations such as binary Avro encoding, MGET usage, virtual partitioning, and parallel deserialization to meet a sub‑10 ms SLA.
This article reviews the development of MLOps, introduces the emerging LMOps framework for large‑model engineering, outlines key architectural components, discusses current challenges and industry trends, and presents future directions and standardization efforts in AI operations.
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.
The supply‑chain team leveraged core MLOps practices—versioning, testing, automated reproducible pipelines, deployment monitoring, and documentation—to eliminate data leakage, ensure online consistency, and accelerate model upgrades, using traffic‑replay, FAAS‑based decoupling, and approval workflows, ultimately cutting order‑fulfillment times, reducing costs, and enabling business teams to adopt reliable AI models at scale.
Baidu Intelligent Cloud outlines how its evolving, high-performance infrastructure—featuring rapid 3-minute instance provisioning, over 200 GB bandwidth, elastic computing, specialized storage, and AI-driven MLOps tools—enables AI-native model training and deployment across booming sectors such as automotive and finance, supporting the industry’s shift to AI-centric cloud services.
The talk outlines Baidu Smart Cloud's comprehensive cloud‑native redesign—including ultra‑elastic compute, AI‑focused storage, high‑performance networking, AI‑driven operations, and edge‑distributed services—illustrated with automotive and fintech case studies that demonstrate how enterprises can accelerate digital transformation in the AI‑native age.
This article explores the concept of intelligent risk control in banking, detailing its AI‑driven architecture, current challenges such as external data costs and model‑deployment friction, and proposes an integrated operational framework that leverages big data, knowledge graphs, and MLOps to enhance risk detection and decision‑making.
This article outlines ten critical components of a modern data lake reference architecture for AI/ML, detailing each function, the supporting vendor tools and open‑source libraries, and how they enable scalable storage, MLOps, distributed training, model hubs, vector search, and data visualization.
The article explores the concept, challenges, and future directions of intelligent banking risk control, emphasizing data integration, AI-driven modeling, feature engineering, MLOps, knowledge graphs, and large‑model applications to create a unified, automated risk management platform.
This article breaks down the AI ecosystem into four layers—AI hardware and cloud services, model and algorithm advances, MLOps middleware, and B2B/B2C applications—highlighting how hardware cost reductions, cloud integration, model breakthroughs, and middleware providers shape the market and adoption speed.
Since 2022, generative AI has become a pervasive trend, and this article explores its integration into DevOps, outlining the technology’s advantages, limitations, emerging trends, and best practices while highlighting how AI‑driven automation reshapes software engineering workflows.
Didi’s new Ray‑native XGBoost training platform replaces the fault‑prone Spark solution with a fully Pythonic, fault‑tolerant architecture that leverages Ray’s autoscaling and gang‑scheduling, delivering 2–6× speedups, reduced failure rates, efficient sparse‑vector handling, scalable hyper‑parameter search, and improved resource utilization for large‑scale machine‑learning workloads.
This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.
This article reviews the evolution from MLOps to LMOps, outlines the fundamentals, challenges, and key technologies of large‑model operations—including inference optimization, prompt engineering, and context‑length extension—and presents Baidu AI Cloud's platform solutions and future outlook.
This article presents the Jiutian Network Intelligence platform’s MLOps implementation at China Mobile, detailing its AI engineering workflow, platform functional and technical architecture, technology selections, model deployment, monitoring, and operational challenges, and shares insights on scaling AI services across 31 provinces.
This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.
Kubeflow is an open‑source, CNCF‑incubated project that provides a Kubernetes‑native MLOps platform integrating notebooks, training operators, AutoML (Katib), pipelines, and model serving (KServe) to streamline the development, deployment, and scaling of machine learning models across diverse frameworks.
This article explains how enterprises can use the Alauda MLOps platform to quickly set up, fine‑tune, and deploy private large language models on cloud‑native infrastructure, covering notebook preparation, GPU allocation, model download, inference service creation, distributed training pipelines, and Docker image building.
This article presents an end‑to‑end overview of Alibaba Cloud’s Machine Learning PAI platform, detailing the three‑stage ML workflow, challenges in model development, the role of pre‑trained and open‑source models, PAI’s architecture, a hands‑on demo, and MLOps best practices for efficient model deployment.
The article introduces MindAlpha, a high‑performance distributed machine‑learning platform built for large‑scale, sparse ad‑tech workloads, detailing its architecture, MLOps pipeline, Spark integration, sync/async training strategies, CPU/GPU choices, model‑splitting techniques, and future directions such as model pruning and AutoML.
Tencent’s Taiji machine learning platform, a cloud‑native, distributed parameter‑server system, provides end‑to‑end MLOps for advertising by integrating data ingestion, feature engineering, model training, evaluation, deployment, and monitoring, supporting massive models up to billions of parameters while improving efficiency, scalability, and resource management.
This article presents ZhongAn Financial’s end‑to‑end MLOps workflow and real‑time feature platform architecture, detailing team roles, data pipelines, Flink‑based processing, TableStore storage, anti‑fraud feature design, and answers to common implementation questions, offering a comprehensive guide for building scalable, low‑latency ML services in finance.
In this conference talk, He Zhengyu of Ant Group outlines the challenges of large‑model AI, proposes a green, shared, model‑centric infrastructure built on foundation models, cloud‑native MLOps, and Model‑as‑a‑Service (MaaS) to lower cost and accelerate AI adoption across industries.
This article details Zhongyuan Bank's AI platform construction, covering its objectives, MLOps-driven design, core modules such as data ingestion, processing, model development, training, evaluation, deployment, monitoring, as well as resource orchestration with Kubernetes and Docker, and the accompanying ModelOps governance framework.
This report examines how MLOps is accelerating AI production in China, highlighting industry adoption across sectors, the booming tool ecosystem, the rise of feature platforms, enhanced observability, performance needs for large models, AI asset management, and the emerging national standards and evaluation results.
This article explains how AI‑driven forecasting, joint error‑distribution safety‑stock calculations, and MLOps‑backed simulation are combined to optimize DTC replenishment, improve inventory days and stock‑out rates, and address practical deployment challenges in fast‑growing e‑commerce supply chains.
This article describes the challenges, overall architecture, data development, model monitoring, continuous training, security‑trustworthiness, and future roadmap of Ant Security's intelligent risk‑control platform, illustrating how AI, big data, and cloud computing are integrated to create a scalable, automated MLOps solution for dynamic fraud detection and mitigation.
This article explains the concept, significance, and practical case studies of MLOps—showing how integrating DevOps principles with data and machine learning creates reliable, automated pipelines for data quality, model monitoring, error analysis, and continuous integration, ultimately accelerating AI delivery.
This article examines how China’s AI strategy and newly released MLOps standards are driving AI engineering, featuring Baidu Cloud’s flagship-level platform, its evaluation results, practical benefits, challenges, and future directions for MLOps in enterprise AI development.
The article describes Laiye's shift to a data‑driven deep‑learning workflow and presents the design, macro‑ and micro‑analysis features, visual diff tools, distributed tracing, and code examples of their OCR testing system that accelerate model evaluation and iterative optimization.
The DataFun Summit 2022 brings together leading AI researchers and industry experts to discuss deep‑learning frameworks, ultra‑large model training, AI chips, compilers, MLOps, and end‑to‑end AI infrastructure, offering live streaming of six thematic forums and dozens of technical talks.
This article outlines the background, standards, evaluation items, process, and registration details of a newly launched MLOps development management maturity assessment designed to accelerate AI model delivery and improve operational efficiency across teams.
The talk by Magic Data’s founder Zhang Qingqing outlines the shift from model‑centric to data‑centric AI, introduces Data‑Centric MLOps methodology, and demonstrates its automotive smart‑cabin application, highlighting data quality requirements, collaborative workflow, and performance gains across speech, live‑social and navigation scenarios.
China’s 14th Five-Year Plan and 2035 Vision prioritize AI, prompting a shift from proof‑of‑concept to product deployment; the newly released Model Delivery standard, part of the Model/MLOps maturity model, defines five maturity levels and a reusable pipeline to boost AI engineering across industries.
vivo’s machine‑learning platform, built for its massive app‑store and e‑commerce ecosystem, streamlines data processing, model training, and deployment through quota‑based resource management, a custom ultra‑large‑scale TensorFlow‑vlps framework, OpenAPI‑driven training, and Jupyter‑integrated interactive development, boosting efficiency for billions of samples and features.
The 2022 GOPS Global Operations Conference in Shenzhen gathered over a thousand attendees for two days of 18 sessions, featuring more than 80 speakers who shared insights on DevOps, cloud native, AI engineering, MLOps, and industry‑specific operational practices across finance, telecom, and technology sectors.
ICBC’s software development center outlines its multi‑year journey adopting XOps practices—DevOps, DevSecOps, DataOps, MLOps, AIOps, ChatOps and BizDevOps—to boost development efficiency, enhance security, accelerate data‑driven AI, and cut costs, showcasing measurable improvements in release frequency, defect rates, and operational automation.
In a detailed interview, the Agricultural Bank of China's R&D center explains how its AI service platform achieved a Level‑3 leading rating in the national MLOps maturity assessment, and how MLOps practices have accelerated model development, improved quality, reduced risk, and driven scalable AI adoption across financial services.
This article analyzes the difficulties of using vanilla Kubeflow for MLOps, introduces the kubeflow‑chart Helm chart that streamlines deployment and integrates tools like SQLFlow and kfpdist, and provides detailed installation commands and a roadmap of upcoming components for a full cloud‑native AI platform.
This article details NetEase Cloud Music’s four‑layer machine‑learning platform architecture, covering resource provisioning with Visual Kubelet and Alibaba Cloud ECI, Ceph storage optimizations, TensorFlow migration, large‑scale graph neural network support, and end‑to‑end workflow tooling that together enable efficient, cost‑effective AI development and deployment.
This article presents a comprehensive overview of Beike's machine learning platform and its inference service, detailing the platform's architecture, GPU virtualization, cloud‑native migration, MLOps implementation, and future roadmap to achieve cost‑effective, automated AI model deployment at scale.
Music FeatureBox is NetEase Cloud Music’s custom feature store that centralizes metadata, unifies offline and online feature storage across multiple engines, provides a cross‑language DSL for extraction, ensures training‑inference data consistency, and offers built‑in monitoring, thereby streamlining feature engineering and accelerating the platform’s machine‑learning lifecycle.
This article explains the globally first AI model development management standard—Model/MLOps Capability Maturity Model (Part 1: Development Management)—detailing its structure, key domains such as requirement management, test case design, and project planning, and how organizations can assess and improve their AI engineering capabilities.
This article introduces the DolphinScheduler OpenMLDB Task, explains how it integrates OpenMLDB's feature platform into DolphinScheduler workflows to create a complete MLOps pipeline, and provides a step‑by‑step demonstration using the TalkingData ad‑fraud detection dataset from Kaggle.
The AI Engineering Online Forum, co‑hosted by China Academy of Information and Communications Technology, unveiled the industry’s first AI Model Development and Management (Model/MLOps) maturity standard, featured expert insights from finance, telecom, and tech leaders, and showcased practical MLOps implementations across banking, Huawei, and AI startups.
This article walks through building the first stage of an open‑source MLOps pipeline—data ingestion and storage—by outlining requirements, selecting tools such as Argo Workflows, Minio and Great Expectations, showing how to set up a minikube cluster, and providing Python scripts and an Argo CronWorkflow to extract, transform, and load OpenAQ air‑quality data into Minio.
ModelOps and MLOps are transforming AI engineering by introducing continuous training, integration, and deployment, which streamline development cycles, standardize model management, and enable ongoing monitoring to enhance inference accuracy and maximize the business value generated by AI models.
This article analyzes the complexities of deploying machine‑learning models in production, outlines the limitations of the existing ABox architecture, and details a comprehensive cloud‑native redesign using Seldon on Kubernetes—including custom HDFS initializers, GPU management, logging, and resource monitoring—to streamline operations and enable unified CPU/GPU model serving.
This article explains how Ctrip tackled the long, costly ML model development‑to‑deployment pipeline by adopting and extending MLflow for full lifecycle management, covering model persistence, tracking, serving, custom pyfunc models, Dockerized deployment, scaling, and performance monitoring.
The article introduces DGL Operator, an open‑source Kubernetes‑based controller that automates the lifecycle of distributed graph neural network training with DGL, explains its terminology, challenges of native DGL distribution, and provides detailed architecture, workflow, and YAML/CLI examples for easy deployment.
The article examines how data teams can select the appropriate Ops framework—DataOps, MLOps, or AIOps—by comparing their origins, principles, responsibilities, and tooling, and stresses that cultural principles outweigh technology choices for efficient delivery of data and machine‑learning products.
The article explains the origins and differences of DevOps, DataOps, MLOps and AIOps, outlines their shared seven principles, and provides practical guidance on adopting the right X‑Ops culture to accelerate data‑driven and machine‑learning‑powered software delivery.
Sunfish is Youzan’s integrated AI platform that unifies visual drag‑and‑drop model training, notebook‑based algorithm development, automated model management and one‑click publishing with a low‑latency, high‑availability “small‑box” inference service, enabling end‑to‑end deep‑learning workflows from data exploration to online recommendation and risk‑control deployment.
This article introduces Kubeflow, a Kubernetes‑based machine‑learning platform, outlines the typical ML lifecycle, details core components, explains why Kubernetes benefits AI workloads, and provides a step‑by‑step guide for installing and accessing Kubeflow’s services, concluding with its industry impact.
