# MLOps MLOps is a set of practices for automating and simplifying machine-learning workflows, deployments, and operations across the ML lifecycle. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Announcing General Availability of VMware Cloud Foundation 9.1.1 DevFeed: [Announcing General Availability of VMware Cloud Foundation 9.1.1]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-03T13:47:18Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [Cloud](), [Security](), [AI Platform](), [AI Models](), [MLOps]() Tags: [ai](), [ai-ml](), [ai-models](), [announce](), [apis](), [cloud](), [cloud-infrastructure](), [complexity](), [cost](), [cpu](), [data-privacy](), [evpn](), [governance](), [home-page](), [integrations](), [kubernetes](), [memory](), [nsx](), [private-ai-services](), [release](), [root-cause-analysis](), [security](), [troubleshooting](), [vcf-9-1](), [vcf-operations](), [vmware](), [vmware-cloud-foundation]() ### AI overview VMware announces the general availability of VMware Cloud Foundation 9.1.1. The release adds tougher security, vSAN Object Storage as a tech preview, multi-tenant AI model sharing with tenant-isolated access controls, and an AI Assistant for diagnostics, management-pack creation, and troubleshooting across infrastructure and Kubernetes clusters. ### Source excerpt Coming on the heels of a very successful VMware Explore in Vegas and the VMware Cloud Foundation (VCF) 9.1 launch in May, we're excited to announce the general availability of VCF 9.1.1. This release builds on VCF 9.1 with tougher security, vSAN Object Storage (tech preview, previously announced) and new capabilities designed to make your ... Continued The post Announcing General Availability of VMware Cloud Foundation 9.1.1 appeared first on VMware Blogs. ## 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 ## Hot trends in platform engineering for AI: Two pathways, one transformation DevFeed: [Hot trends in platform engineering for AI: Two pathways, one transformation]() Original publisher: [Read original article]() Author: Mallory Haigh Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [Platform Engineering](), [AI Platform](), [Artificial Intelligence](), [MLOps](), [DevOps](), [AI Agent](), [GPU](), [Kubernetes](), [Orchestration](), [CI/CD](), [datasets](), [Infrastructure as code]() Tags: [ai](), [ai-agent](), [ai-platform](), [automation](), [ci-cd](), [datasets](), [devops](), [gpu](), [infrastructure-as-code](), [kubernetes](), [orchestration](), [platform](), [platform-engineering]() ### AI overview This article describes two connected paths in platform engineering for AI: using LLMs and agents to improve existing platform capabilities, and building specialized platforms for AI/ML workloads. It highlights infrastructure-as-code generation, intelligent troubleshooting, security policy automation, GPU orchestration, model registries, feature stores, experiment tracking, and the need to support data scientists and ML engineers. It argues that conventional software-delivery platforms may not fit ML workloads and points toward more autonomous platforms. ### Source excerpt Discover the hot trends shaping platform engineering for AI and learn more about the shift toward autonomous, governance-first, and unified DevOps/MLOps workflows. ## 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. ## 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 ## 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 ## Introducing Mistral AI Studio. DevFeed: [Introducing Mistral AI Studio.]() Original publisher: [Read original article]() Published: 2025-10-24T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI Platform](), [Artificial Intelligence](), [MLOps](), [Security](), [DevOps](), [data](), [Large Language Model](), [Deployment](), [Prompt Engineering](), [on-prem](), [VPC]() Tags: [agents](), [ai](), [ai-adoption](), [ai-assistants](), [ai-platform](), [ai-studio](), [autonomous-agents](), [compliance](), [infrastructure](), [multimodal-ai](), [on-prem](), [privacy](), [provenance](), [vpc](), [workflows]() ### AI overview Mistral introduces AI Studio, an enterprise AI platform for customizing, fine-tuning, evaluating, governing, and deploying AI assistants, autonomous agents, and multimodal AI with open models. It addresses the gap between AI prototypes and production by providing evaluation, feedback and dataset workflows, provenance and versioning, governance, and flexible deployment across hybrid, VPC, and on-prem infrastructure. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## 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 ## Model serving architectures DevFeed: [Model serving architectures]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-08-01T17:39:58Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [model-serving](), [model-deployment](), [Deployment](), [Python](), [Low Latency](), [SDK](), [real-time]() Tags: [batch](), [databricks](), [deployment](), [latency](), [mlops](), [model-serving](), [python](), [real-time](), [sdk]() ### AI overview Lecture 5 in an MLOps with Databricks course explains model serving architectures on Databricks. It covers batch prediction and feature serving, model serving, and model serving with feature lookup, including serverless infrastructure and endpoint-based access. ### Source excerpt Lecture 5 of MLOps with Databricks course ## Logging and registering models with MLflow DevFeed: [Logging and registering models with MLflow]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-31T18:49:09Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Model Development](), [scikit-learn]() Tags: [databricks](), [machine-learning](), [mlops](), [model](), [model-training](), [pipeline]() ### AI overview Lecture 4 in an MLOps with Databricks course explains how to log and register machine learning models with MLflow. It covers the standardized MLflow Model format, supported model flavors, custom PythonModel implementations, and a scikit-learn pipeline example. ### Source excerpt Lecture 4 of MLOps with Databricks course ## Getting started with MLflow DevFeed: [Getting started with MLflow]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-30T13:47:51Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Command-line interface](), [Filesystems](), [Code](), [Open Source](), [vs-code]() Tags: [best-practices](), [cli](), [code](), [databricks](), [env-file-security](), [getting-started](), [mlops](), [open-source](), [vs-code]() ### AI overview Lecture 3 in an MLOps course with Databricks introduces MLflow experiment tracking. It explains the Experiment and Run entities, how they are created and retrieved, and how to configure MLflow tracking and registry URIs for Databricks and local development. ### Source excerpt Lecture 3 of MLOps with Databricks course ## Developing on Databricks DevFeed: [Developing on Databricks]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-29T15:25:32Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [dev-tools](), [Development](), [Command-line interface](), [Authentication](), [CI/CD]() Tags: [article](), [authentication](), [ci-cd](), [cli](), [databricks](), [developer-tools](), [mlops]() ### AI overview A tutorial for Lecture 2 of an MLOps with Databricks course. It explains why notebook-based development can hinder modularity, code quality, and unit testing, then introduces local development with the Databricks VS Code extension, CLI, and Databricks Connect while continuing to run PySpark code on Databricks. It also covers setup, repository forking, catalogs and schemas, CLI installation, and authentication. ### Source excerpt Lecture 2 of MLOps with Databricks course ## Introduction to MLOps DevFeed: [Introduction to MLOps]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-28T17:19:47Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [MLOps](), [databricks](), [Monitoring](), [Deployment](), [Testing]() Tags: [audit-trail](), [course](), [databricks](), [deployment](), [introduction](), [mlops](), [monitoring](), [production](), [reproducibility](), [testing]() ### AI overview Lecture 1 of a hands-on MLOps with Databricks course explains what production means for machine-learning workflows. It uses a demand-forecasting example to show why scheduled notebooks may lack testing, monitoring, error handling, version control, deployment processes, rollback, and audit trails, and introduces MLOps principles for improving reliability and control. ### Source excerpt Lecture 1 of MLOps with Databricks course [Next page]()