# MLOps Published articles for MLOps. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-14T16:39:15Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Artificial Intelligence](), [GPU](), [Large Language Model](), [Nvidia](), [Routing (disambiguation)](), [GB200](), [Network](), [Python](), [deepseek](), [mixtral](), [qwen]() Tags: [agentic-ai-generative-ai](), [ai](), [communication](), [deepseek](), [developer-tools-techniques](), [gb200](), [gpu](), [mixtral](), [mixture-of-experts-moe](), [mlops](), [moe](), [networks](), [nvfp4](), [nvidia](), [performance](), [python](), [qwen](), [router](), [routing](), [tensors](), [token](), [tokens](), [training-ai-models]() ### AI overview This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement. ### Source excerpt Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE... ## Creating an AI Platform for classic ML online inference DevFeed: [Creating an AI Platform for classic ML online inference]() Original publisher: [Read original article]() Author: Rohith Leeladharan Published: 2026-09-10T07:26:46Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Platform](), [Inference](), [MLOps](), [Machine learning](), [model-deployment]() Tags: [ai](), [ai-platform](), [ai-platform-engineering](), [deploy](), [experiments](), [feature-store](), [inference](), [machine-learning](), [ml](), [mlops](), [monitoring](), [predictions]() ### AI overview This article describes how American Express Global Business Travel built an AI platform for deploying classic machine-learning systems and supporting online inference. It explains the platform's requirements--simplicity, self-service, experimentation, and continuous improvement--and details the pre-process, predict, post-process pattern used by inference engines. ### Source excerpt Introduction In 2021, we were given the mission to have AI Systems running in production. The team, instead of just following a classical MLOps process, that involves transforming a Jupyter notebook into a product running in production, decided to go further by creating a platform to deploy AI systems in production. The team decided the platform should respect these requirements: Simplicity: The code powering AI systems should be simple, readable, and easy to maintain -- less intricacy means fewer bugs in production and greater reliability. Self-service: Anyone should be able to build and deploy AI systems autonomously, without depending on a central team. Experimentation: The platform should make it easy to run and iterate on experiments. Continuous improvement: Data related to events and interactions within AI systems must be captured, enabling monitoring and continuous improvement over time. In this article, we will walk through the work done to build a platform that fulfills these four requirements. Background At American Express Global Business Travel, we use machine learning (ML) models for a variety of user experiences like ranking hotel and flight search results. Our ML models are wrapped in inference engines that handle both pre-processing of input data before we run a prediction with the model, and post-processing of output data before returning the output to the caller. The overall flow looks something like this: Figure 1: Handling an inference request A client service that would like the ML model's predictions provides necessary context about the request like which user the request is for. Then, optionally, the inference engine fetches any necessary features for inference from our feature store [part 1][part 2]. Finally, it pre-processes the data, runs the predictions using the trained ML model, and does any necessary post-processing of the model output before returning the response to the caller. We call this the pre-process, predict, post-process patter ## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-04T17:54:45Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [AI Factory](), [AI Infrastructure](), [AI Platform](), [NVIDIA Vera Rubin](), [NVIDIA DGX](), [NVIDIA Vera](), [MLOps](), [NVIDIA DSX](), [ai-governance](), [Server]() Tags: [2026](), [ai](), [ai-factory](), [ai-governance](), [ai-infrastructure](), [asus](), [enterprise](), [mlops](), [nvidia-dsx](), [nvidia-vera](), [nvl72](), [object-storage](), [platform](), [storage](), [vera-rubin-nvl72]() ### AI overview ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware. ### Source excerpt ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer appeared first on StorageReview.com. ## How to Size GPUs for AI Inference and TCO Without Overspending DevFeed: [How to Size GPUs for AI Inference and TCO Without Overspending]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-01T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Inference](), [GPU](), [Inference Performance](), [Large Language Model](), [cloud-infrastructure](), [Optimization]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [concurrency](), [cost](), [data-center-cloud](), [gpu](), [inference-performance](), [latency](), [llm](), [mlops](), [quantization]() ### AI overview A practical guide to sizing GPU infrastructure for AI inference workloads while balancing latency, concurrency, model choice, deployment strategy, and total cost of ownership. ### Source excerpt The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently... ## Using ArgoCD to Manage AI Model Deployments with GitOps DevFeed: [Using ArgoCD to Manage AI Model Deployments with GitOps]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-07-28T18:24:57Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [GitOps](), [argocd](), [model-deployment](), [Artificial Intelligence](), [model-serving](), [Architecture & Design]() Tags: [ai](), [ai-models](), [argo-rollouts-model-promotion](), [argocd](), [argocd-ai-model-deployment](), [argocd-applicationset](), [argocd-sync-waves](), [automation](), [ci-cd](), [declarative-model-serving](), [deployment](), [devops](), [gitops](), [gitops-for-ai-model-deployments](), [gitops-mlops](), [kserve](), [kserve-canary-deployment](), [kubernetes](), [kubernetes-model-deployment-rollback](), [ml-model-deployment](), [ml-model-versioning](), [mlops](), [model-deployment](), [model-registry-to-git-automation](), [model-serving](), [pull-requests](), [standard](), [tutorial]() ### AI overview This tutorial explains how to use ArgoCD and GitOps to deploy AI models while accounting for model weights stored outside container images. It covers pinned model references, rollback and recovery, synchronization ordering, readiness probes, evaluation-based promotion gates, and KServe status handling. ### Source excerpt Your microservices deploy through pull requests with full audit trails. Your models deploy because someone ran a script. Here is how to close that gap with ArgoCD, and what changes when the artifact weighs four gigabytes. ## Инференс LLM: от KV-кэша до продакшен-деплоя DevFeed: [Инференс LLM: от KV-кэша до продакшен-деплоя]() Original publisher: [Read original article]() Author: a\_ryzhov (hh.ru, Конференции Олега Бунина (Онтико)) Published: 2026-07-27T05:30:45Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [Large Language Model](), [Caching](), [on-prem](), [genai](), [MLOps](), [sglang](), [vllm]() Tags: [2026](), [compute](), [genai](), [http](), [inference](), [kv-cache](), [llm](), [mlops](), [on-prem](), [sglang](), [sram](), [vllm]() ### AI overview This Russian-language developer article explains how LLM inference behaves in on-premises production environments in 2026. It argues that GPU memory management is the main efficiency constraint, describes how KV caching shifts decoding from compute-bound to memory-bandwidth-bound work, and introduces vLLM and SGLang as ways to address the problem. ### Source excerpt Привет! Я Саша Рыжов, MLOps-инженер в hh.ru, уже три года занимаюсь развитием инфраструктуры для искусственного интеллекта. Компании, которые развивают GenAI, рано или поздно приходят к задачам по запуску LLM на собственном железе. В статье я расскажу, как обстоят дела с движками инференса в 2026 году и как запустить on-prem-прод и не изобрести при этом велосипед. Читать далее ## KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience DevFeed: [KubeCon + CloudNativeCon India 2026: Our Kubeflow Community Experience]() Original publisher: [Read original article]() Author: Khushi Agrawal Published: 2026-07-27T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [Kubernetes](), [MLOps](), [distributed-training](), [model-serving](), [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [GPU](), [Cloud]() Tags: [2026](), [ai](), [cloud-native](), [community](), [distributed-training](), [gpu](), [india](), [kubecon](), [kubernetes](), [llms](), [machine-learning](), [mlops](), [model-serving](), [model-training](), [rag]() ### AI overview A Kubeflow community recap of KubeCon + CloudNativeCon India 2026 in Mumbai. It describes Kubeflow's presence at the CNCF Project Pavilion, common architecture questions, featured talks, booth discussions about production machine learning workflows, distributed training, model serving, GPU resource allocation, LLMs, and RAG pipelines, and efforts to onboard contributors. ### Source excerpt Introduction ## Announcing the State of Platform Engineering Report Vol 4 DevFeed: [Announcing the State of Platform Engineering Report Vol 4]() Original publisher: [Read original article]() Author: Sam Barlien Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [Artificial Intelligence](), [AI Development](), [GPU](), [supply-chain-security](), [MLOps](), [finops]() Tags: [2026](), [ai](), [ai-ml](), [cloud-native](), [engineering](), [infrastructure](), [mlops](), [platform](), [platform-engineering](), [platforms](), [report](), [scale](), [software-supply-chain](), [speed]() ### AI overview The State of Platform Engineering Report Volume 4 presents platform engineering as a foundational discipline for modern enterprises. Based on insights from 518 engineers, it describes a shift from the cloud-native era to an AI-native era, emphasizing "shifting down," AI-powered platforms, platforms for AI, specialized GPU infrastructure, governance, and MLOps requirements. ### Source excerpt Insights from over 500 engineers. Explore the shift to 'AI-native,' the 'dual mandate,' 'shifting down,' and key trends for 2026. ## AI Engineer vs. Platform Engineer: Differences in AI Roles DevFeed: [AI Engineer vs. Platform Engineer: Differences in AI Roles]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-07-22T00:21:05Z Content type: comparison Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [AI Platform](), [Prompt Engineering](), [MLOps]() Tags: [agentic-ai](), [ai](), [ai-engineer](), [artificial-intelligence](), [career](), [generative-ai-tools](), [llm](), [mlops](), [platform](), [platform-engineering]() ### AI overview The article compares AI Engineer and Platform Engineer roles amid overlapping job titles. It describes AI Engineers as working on model choice, prompts, and agent behavior, while Platform Engineers focus on the infrastructure and operations needed to run AI systems. ### Source excerpt By Geeta Kakrani (GDE in AI & TPU) Open any job board right now and search "AI." Within a few listings, you'll notice something odd: the roles don't line up. Two postings with almost identical requirements have completely different titles. One company's "AI Engineer" is another company's "AI Platform Engineer" is a third company's "MLOps Engineer." This isn't a small naming quirk. For anyone trying to plan a career -- or even just understand where they fit -- it's a real, growing source of confusion. Not because AI itself is hard to understand, but because nobody clearly explains who is responsible for what anymore. The job title problem nobody talks about You'll find titles like: AI Engineer AI Platform Engineer AI Infrastructure Engineer MLOps Engineer LLM Platform Engineer Applied AI Engineer These titles overlap heavily. They pay in similar ranges. They list nearly identical skills. And most of them didn't exist as separate roles even three years ago. This isn't because companies are confused. It's because the industry is still figuring out what to call a very real, very new job -- one that sits between two older disciplines that used to be completely separate. If you're a student or early-career professional trying to plan your path, this title chaos makes it genuinely hard to know what to learn, what to apply for, and what a company actually expects from you on day one. So what's the actual difference? Strip away the job titles, and there are really two different jobs hiding underneath the AI buzzword. The AI Engineer works on the intelligence itself. Choosing which model to use. Designing prompts and instructions. Deciding how an agent should behave, what it should refuse to do, when it should use a tool versus answer directly. This is the "thinking" layer. The Platform Engineer works on everything that lets that intelligence actually run in the real world. Servers, deployment, scaling, security, monitoring, cost control. This is the layer most people never see ## Laguna S 2.1 is now available on AI Gateway DevFeed: [Laguna S 2.1 is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [agentic-coding](), [Mixture of Experts (MoE)](), [context window](), [API](), [MLOps](), [AI Research](), [API keys](), [Routing (disambiguation)]() Tags: [agentic-coding](), [ai-models](), [ai-research](), [api-keys](), [context-window](), [cost](), [mixture-of-experts](), [mlops](), [routing](), [sdk](), [support]() ### AI overview Poolside's Laguna S 2.1 is now available through Vercel AI Gateway in free and paid versions, with context windows of 256K and 1M tokens. The open-weight Mixture-of-Experts model supports thinking and no-thinking modes and is designed for agentic coding, long-running tasks, browser tooling, MLOps pipelines, and AI research. ### Source excerpt Laguna S 2.1 from Poolside is now available on AI Gateway. There are 2 versions of the model available: Free version (256K context window): poolside/laguna-s-2.1-free Paid version (1M context window): poolside/laguna-s-2.1 Laguna S 2.1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes. The model specializes in agentic coding and long-running tasks, including writing and debugging code, running tests, building browser-based tooling, and working on MLOps pipelines and AI research. In thinking mode, Laguna S 2.1 reports 70.2% on Terminal-Bench 2.1, 78.5% on SWE-bench Multilingual, and 59.4% on SWE-Bench Pro. To use Laguna S 2.1, set model to poolside/laguna-s-2.1-free or poolside/laguna-s-2.1 in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Laguna S 2.1 in the model playground. Read more ## Как оптимизировать инференс LLM: кеширование, время ответа и GPU-ресурсы DevFeed: [Как оптимизировать инференс LLM: кеширование, время ответа и GPU-ресурсы]() Original publisher: [Read original article]() Author: hotckisss (Яндекс, Yandex Cloud & Yandex Infrastructure) Published: 2026-07-08T07:04:08Z Content type: tutorial Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [Large Language Model](), [Inference Performance](), [Low-Latency Inference](), [GPU](), [Artificial Intelligence]() Tags: [ai](), [inference](), [llm](), [ml](), [mlops](), [tag-b92bf5906bbd](), [time](), [token]() ### AI overview This article explains how to optimize LLM inference in production under mixed workloads. It examines causes of increased Time to First Token, including scheduling, GPU memory allocation, streaming output queues, networking, long contexts, and KV-cache growth, and outlines optimizations such as separating prefill and decode, contextual parallelism, speculative decoding, cache-aware load balancing, and large-model delivery. ### Source excerpt Вы запустили LLM-инференс в продакшене. Поток запросов не менялся, нагрузка та же, что вчера, -- а Time to First Token внезапно вырос в три раза. Первая мысль: что-то с моделью. На деле причина почти никогда не в модели -- она прячется в планировщике, аллокаторе GPU-памяти, очереди стримингового вывода или сети. Чем длиннее контекст, тем больнее. Для классического attention вычислительная сложность растёт очень быстро. KV-кеш раздувается до десятков и сотен гигабайт, а в облаке всё это происходит на совершенно произвольном трафике: у одного клиента кодовый ассистент, у другого -- аналитика на миллион запросов в день, у третьего -- голосовой робот. На таких смешанных нагрузках всплывает то, чего не видно на референсных замерах вендора. Читать далее ## Enhancing Goodput in Large-Scale LLM Training with Nonuniform Tensor Parallelism DevFeed: [Enhancing Goodput in Large-Scale LLM Training with Nonuniform Tensor Parallelism]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-06T21:44:23Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Large Language Model](), [Training AI Models](), [LLM Techniques](), [NVLink](), [Availability](), [Hardware](), [Network](), [systems](), [Nvidia]() Tags: [ai](), [ai-training](), [availability](), [blackwell](), [data-center-cloud](), [developer-tools-techniques](), [efficiency](), [featured](), [gpu](), [hardware](), [llm](), [llm-techniques](), [mlops](), [network](), [nvidia](), [nvidia-blackwell](), [nvlink](), [performance](), [scale](), [training](), [work]() ### AI overview This article explains how Nonuniform Tensor Parallelism can improve Goodput in large-scale LLM training by adapting tensor parallelism to changing GPU availability and overlapping data resharding. The experimental approach aims to reduce interruptions, lost throughput, and computational waste in tightly interconnected GPU clusters. ### Source excerpt Training LLMs at massive scale brings unique infrastructure challenges, especially as jobs span thousands of GPUs and run for extended periods. The longer these... ## How to Govern Autonomous Agents in Enterprise AI Factories DevFeed: [How to Govern Autonomous Agents in Enterprise AI Factories]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-06-29T15:50:13Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [AI Factory](), [systems](), [vulnerability management](), [configuration](), [Network](), [SOC](), [ide](), [browser](), [Terminal]() Tags: [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [ai-factory](), [architecture](), [autonomous](), [autonomous-agents](), [browser](), [data-center-cloud](), [enterprise](), [featured](), [ide](), [mlops](), [network](), [openshell](), [policy](), [soc](), [telemetry](), [vulnerability-management]() ### AI overview This tutorial presents NVIDIA's Secure Agent Workspace Reference Design for governing autonomous AI agents in enterprise environments. It explains how to separate the presentation layer from managed agent execution and enforce identity, network access, credentials, runtime policy, auditing, human review, and workspace isolation. ### Source excerpt AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on... ## Best AI Infrastructure Tools in 2026 DevFeed: [Best AI Infrastructure Tools in 2026]() Original publisher: [Read original article]() Author: Alex Leventer Published: 2026-05-25T00:00:00Z Content type: article Language: en Sources: [Pulumi]() Topics: [AI Infrastructure](), [MLOps](), [GPU](), [Cloud](), [Command-line interface]() Tags: [2026](), [ai](), [ai-infrastructure](), [announcements](), [cloud](), [devops](), [general](), [gpu](), [infrastructure-as-code](), [mlops](), [platform-engineering](), [tools]() ### AI overview This guide distinguishes infrastructure for AI, including GPU clouds and MLOps platforms, from AI-powered infrastructure management. It surveys tools in both categories and highlights options such as CoreWeave, Modal, MLflow, Pulumi Neo, and other infrastructure agents and assistants. ### Source excerpt "AI infrastructure tools" covers two distinct markets: infrastructure for AI (GPU clouds like CoreWeave, MLOps platforms like Weights & Biases) and AI for infrastructure (agentic platforms like Pulumi Neo that generate, deploy, and govern cloud resources for you). Most teams need tools from both categories, and picking the wrong one wastes budget and adoption goodwill. The pressure to get this right is real. McKinsey research puts the productivity lift from generative AI in software development at 20-45%, which is great for application teams and a problem for platform teams trying to keep up with the resulting feature flow. Infrastructure investment is climbing on both fronts: more spend on the compute that trains and serves models, more spend on AI tools that manage everything else. This guide covers both categories: the compute and MLOps stack in Part 1, and AI-powered infrastructure management in Part 2, where the more interesting product shift is happening. AI infrastructure tools overview Tools for building AI infrastructure CoreWeave: GPU cloud built for AI workloads Lambda Labs: straightforward GPU cloud for research and startups Modal: serverless GPU compute Weights & Biases: ML experiment tracking and model management MLflow: open-source ML lifecycle platform Hyperscaler AI platforms: AWS SageMaker, Google's Gemini Enterprise Agent Platform, Azure ML AI-powered infrastructure management tools Pulumi Neo: infrastructure agent with policy automation Firefly: asset codification with an emerging agent layer env zero: multi-IaC insights, now with an agent CLI Spacelift Intelligence: conversational Q&A and natural-language provisioning Crossplane with Upbound: Kubernetes-native infrastructure Hyperscaler infrastructure agents: Azure SRE Agent, Gemini Cloud Assist General-purpose code assistants: Copilot, Claude Code, Cursor, Gemini AWS Infrastructure Composer: visual builder for CloudFormation templates Quick picks If you only have two minutes: Enterprise complia ## A Practical Guide to Choosing Among Six AI Career Roles DevFeed: [A Practical Guide to Choosing Among Six AI Career Roles]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-05-07T15:01:02Z Content type: article Language: en Sources: [Big Tech Careers]() Topics: [Tech Careers](), [Artificial Intelligence](), [AI Development](), [MLOps](), [Large Language Model](), [Deployment]() Tags: [2026](), [ai](), [ai-engineer](), [article](), [career](), [deployment](), [llm](), [machine-learning](), [mlops](), [orchestration](), [prompt]() ### AI overview This career guide maps six AI roles--AI Engineer, Machine Learning Engineer, AI Product Manager, AI Platform or MLOps Engineer, AI Forward Deployed Engineer, and AI Solutions Architect or AI Consultant--to different strengths, interests, and working styles. It explains that AI Engineers focus on building features and applications, while Machine Learning Engineers focus on model behavior, data, performance, training, evaluation, and deployment. ### Source excerpt The hottest AI roles in 2026, mapped to your skills, interests, and working style ## Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience DevFeed: [Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-03-19T05:00:00Z Content type: release Language: en Sources: [Kubeflow]() Topics: [SDKs](), [MLOps](), [Apache Spark](), [data-processing](), [Hyperparameter optimization](), [Machine learning](), [Kubernetes](), [Python]() Tags: [apache-spark](), [data-processing](), [hyperparameter-optimization](), [kubernetes](), [mlops](), [python](), [release](), [sdk]() ### AI overview Kubeflow SDK v0.4.0 introduces a Model Registry Client, SparkClient with SparkConnect support, namespaced TrainingRuntimes, dataset and model initializers, and new documentation. The release targets a unified Python interface for AI workloads on Kubernetes across data processing, model management, and ML pipelines. ### Source excerpt Explore the full documentation at sdk.kubeflow.org ## Protect your AI workloads from supply chain attacks DevFeed: [Protect your AI workloads from supply chain attacks]() Original publisher: [Read original article]() Published: 2026-01-30T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [Containers](), [chainguard](), [Vulnerabilities](), [AI Strategy](), [Open Source](), [Security](), [PyTorch](), [Tensorflow](), [MLOps]() Tags: [ai](), [ai-containers](), [chainguard](), [chainguard-containers](), [chainguard-for-ai](), [chainguard-libraries](), [containers](), [cudnn](), [cves](), [data-processing](), [deployment](), [kserve](), [legacy](), [libraries](), [mlops](), [nemo](), [ollama](), [python](), [pytorch](), [security](), [security-vulnerabilities](), [supply-chain](), [supply-chain-attacks](), [zero-cve-containers]() ### AI overview The article discusses security and operational challenges in AI/ML workloads, including complex dependencies, bloated artifacts, infrastructure sprawl, and unremediated CVEs. It presents Chainguard Containers' minimal images for AI workloads as a way to reduce attack surface, storage needs, and deployment overhead, and cites a 50 MB gpu-operator image compared with a 170 MB upstream equivalent. ### Source excerpt Chainguard secures AI adoption with minimal, zero-CVE containers and source-built libraries that prevent supply chain malware while keeping developers fast. ## Hands-On MLOps Course Using Databricks Free Edition DevFeed: [Hands-On MLOps Course Using Databricks Free Edition]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-08-29T13:57:23Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [MLOps](), [databricks](), [GitHub Actions](), [GitHub](), [Monitoring]() Tags: [course](), [databricks](), [github-actions](), [mlops]() ### AI overview An overview of a hands-on MLOps course using Databricks Free Edition. It covers moving machine learning prototypes toward production pipelines, including automated deployment with Databricks Asset Bundles and GitHub Actions, plus monitoring with drift detection. ### Source excerpt A full overview of the course ## Implementing Model Monitoring on Databricks DevFeed: [Implementing Model Monitoring on Databricks]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-06T16:55:23Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Monitoring](), [model-serving](), [dashboards](), [Inference](), [Logging](), [Data Quality]() Tags: [databricks](), [metrics](), [mlops](), [model-monitoring](), [model-serving](), [monitoring](), [request](), [table]() ### AI overview Lecture 10 in an MLOps with Databricks course demonstrates model monitoring using inference tables and Lakehouse Monitoring. It covers collecting inference logs, creating a structured monitoring table, scheduling refreshes, and building a dashboard to visualize metrics and detect drift. ### Source excerpt Lecture 10 of MLOps with Databricks course ## Introduction to ML monitoring DevFeed: [Introduction to ML monitoring]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-05T21:56:29Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Monitoring](), [data]() Tags: [article](), [course](), [data](), [databricks](), [errors](), [introduction](), [latency](), [ml](), [mlops](), [model](), [monitoring](), [performance](), [predictions](), [production](), [workflows]() ### AI overview An introductory lecture in the MLOps with Databricks course explains why machine learning systems require monitoring beyond system health, errors, latency, KPIs, and infrastructure costs. It introduces data drift and concept drift as causes of model performance degradation, even when code and infrastructure remain unchanged. ### Source excerpt Lecture 9 of MLOps with Databricks course ## CI/CD & Deployment Strategies DevFeed: [CI/CD & Deployment Strategies]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-04T19:12:07Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [CI/CD](), [Deployment](), [Access Control]() Tags: [access-control](), [ci](), [ci-cd](), [databricks](), [deployment](), [mlops](), [permissions]() ### AI overview Lecture 8 in an MLOps with Databricks course explains how to organize machine learning data and assets with Unity Catalog, Workspaces, catalogs, schemas, and access controls. It also introduces CI/CD pipelines for automating deployments. ### Source excerpt Lecture 8 of MLOps with Databricks course ## From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform DevFeed: [From siloed DataOps, MLOps, and LLMOps to a unified data-intelligence platform]() Original publisher: [Read original article]() Author: Rajit Saha Published: 2025-08-04T18:03:19Z Content type: opinion Language: en Sources: [Udemy Engineering]() Topics: [DataOps](), [MLOps](), [Generative AI](), [Apache Spark](), [Amazon Bedrock](), [Amazon S3](), [Amazon Redshift](), [Amazon SageMaker](), [apache-flink]() Tags: [ai](), [aiops](), [amazon-s3](), [analytics](), [apache-flink](), [apache-spark](), [bedrock](), [dataops](), [large-language-models](), [llmops](), [mlops]() ### AI overview The article describes how DataOps, MLOps, and AI/LLM Ops commonly rely on separate systems and teams for data processing, model deployment, inference, evaluation, orchestration, governance, and monitoring. It then introduces Databricks' Data Intelligence Platform as a unified environment intended to bring these domains together. ### Source excerpt Introduction In modern data-driven businesses, the pace of innovation in analytics and artificial intelligence has outstripped the capacity of many teams. Three distinct disciplines emerged to handle this expansion: Data platform (DataOps) teams built data lakes on cloud storage such as Amazon S3, processed them with Apache Spark and Hive on EMR, ingested streaming data with Spark Structured Streaming or Apache Flink, and loaded tabular copies into MPP warehouses like Redshift for interactive SQL and BI. Cataloguing and governance were offloaded to external tools such as DataHub, and fine-grained access controls required third-party services like Privacera. This architecture worked, but it required separate workflows for batch and streaming, extra systems for lineage and governance, and a mosaic of operational teams. MLOps teams provided an additional layer. Data scientists used notebook environments (for example, Amazon SageMaker) to preprocess data, train, and evaluate models. Deploying models meant writing integration code to move features into a serving layer, to register models in disparate registries and to build custom APIs for inference. Feature stores and model registries were bought from additional vendors. Updates and monitoring were often manual processes. AI/LLM Ops teams are a new addition because generative AI requires specialized components: LLM gateways (e.g., Amazon Bedrock) to proxy access to foundation models; evaluation tooling to compare large language models; orchestration frameworks for agents; vector databases for retrieval augmented generation; and of course another layer of security, access management and cost control. These tools seldom integrate seamlessly with existing data and ML pipelines. This fragmented state makes it difficult to react quickly when product requirements change. Each new capability requires another system, another integration, and another team. Meanwhile, budgets tighten and go-to-market timelines shrink. The questio ## Databricks Asset Bundles DevFeed: [Databricks Asset Bundles]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-03T14:50:17Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Infrastructure as code](), [YAML](), [Orchestration]() Tags: [ci-cd](), [databricks](), [deployment](), [infrastructure-as-code](), [mlops](), [orchestration](), [yaml]() ### AI overview Lecture 7 in an MLOps with Databricks course explains Databricks Asset Bundles (DAB) as a declarative, YAML-based way to package and deploy code, jobs, configuration, and dependencies. It outlines a machine learning workflow covering preprocessing, model training and evaluation, conditional model updates, and deployment. ### Source excerpt Lecture 7 of MLOps with Databricks course ## Deploying a model serving endpoint DevFeed: [Deploying a model serving endpoint]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2025-08-02T11:00:01Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [model-serving](), [model-deployment](), [Deployment](), [autoscaling](), [API](), [A/B Testing]() Tags: [a-b-testing](), [api](), [article](), [autoscaling](), [databricks](), [deployment](), [latency](), [mlops](), [model-serving]() ### AI overview Lecture 6 in an MLOps with Databricks course explains how to deploy registered MLflow models as Databricks Model Serving REST APIs. It covers serverless deployment, automatic scaling, monitoring, Unity Catalog integration, workload sizing, and limitations such as restricted runtime and cluster control. ### Source excerpt Lecture 6 of MLOps with Databricks course [Next page]()