# MarvelousMLOps Serving you expertise MLOps content This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering DevFeed: [What 300+ Engineers from Netflix, Amazon, and Instacart Asked About AI Engineering]() Original publisher: [Read original article]() Author: Hugo Bowne-Anderson Published: 2026-03-06T07:03:44Z Content type: article Language: en Sources: [MarvelousMLOps]() Topics: [AI Engineering](), [AI Development](), [Large Language Model](), [Prompt Engineering](), [Testing]() Tags: [ai-engineering](), [context-engineering](), [engineering](), [evaluation](), [llms](), [testing]() ### AI overview This article presents the top 10 questions and answers gathered from four cohorts of a Building AI Applications course attended by more than 300 builders from companies including Netflix, Amazon, and Instacart. The supplied excerpt details how to improve reliability and consistency in LLM applications through prompt and context engineering, structured outputs, validation, evaluation, and testing. ### Source excerpt The Top 10 questions (and answers) from 4 cohorts of Building AI Applications ## Set Up a Python Project with uv DevFeed: [Set Up a Python Project with uv]() Original publisher: [Read original article]() Author: Boldizsár Published: 2025-11-10T07:28:01Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [Tutorial](), [Python]() Tags: [2025](), [command-line](), [environment](), [homebrew](), [install](), [linux](), [macos](), [python](), [terminal](), [tutorial](), [windows]() ### AI overview A tutorial for setting up a modern Python project with uv. It covers installing uv on macOS, Linux, and Windows, creating a project, reviewing its files, and creating and synchronizing a project-specific virtual environment and dependencies. ### Source excerpt In 2025 the best way to manage Python projects is using uv. This tutorial helps you set up a modern Python project from scratch using uv. ## Patterns and Anti-Patterns for Building with LLMs DevFeed: [Patterns and Anti-Patterns for Building with LLMs]() Original publisher: [Read original article]() Author: Hugo Bowne-Anderson Published: 2025-10-27T10:24:35Z Content type: article Language: en Sources: [MarvelousMLOps]() Topics: [Large Language Model](), [AI Development](), [Development]() Tags: [agents](), [ai](), [llms](), [software-development]() ### AI overview The article presents seven common failure modes in AI application development, described as the "Seven Deadly Sins of AI App Development," and pairs them with practical solutions. The supplied text discusses unrealistic demands for 100% accuracy and granting agents too much autonomy, recommending transparent systems, human oversight, smaller task steps, and opportunities for clarification. ### Source excerpt A bit about our guest author: Hugo Bowne- Anderson advises and teaches teams building LLM-powered systems, including engineers from Netflix, Meta, and the United Nations through my course on the AI software development lifecycle. It covers everything from retrieval and evaluation to agent design and all the steps in between. Use the code ## 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 ## 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 ## Stop Building AI agents DevFeed: [Stop Building AI agents]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-07T10:59:56Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [Large Language Model](), [AI Development](), [Development](), [Software Engineering](), [anthropic]() Tags: [agent](), [ai](), [ai-agents](), [anthropic](), [code](), [debugging](), [development](), [llm](), [research](), [workflow]() ### AI overview This developer article argues that teams should not begin with AI agents by default. Drawing on failures with a CrewAI research system and other projects, it presents five LLM workflow patterns, explains when agents are appropriate, and discusses safer ways to build them. ### Source excerpt Maria: Today, the scene is owned by Hugo, a brilliant mind who advises and teaches teams building LLM-powered systems, including engineers from Netflix, Meta, and the U.S. ## Here comes another bubble (2025) DevFeed: [Here comes another bubble (2025)]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-06-21T17:59:03Z Content type: opinion Language: en Sources: [MarvelousMLOps]() Topics: [Artificial Intelligence](), [Large Language Model](), [AI Agent](), [anthropic](), [ChatGPT](), [Model Context Protocol](), [Google]() Tags: [2025](), [agent](), [ai](), [chatgpt](), [claude](), [llms](), [mcp](), [startup](), [tokens](), [trends]() ### AI overview An AI-generated parody song examines the technology industry's 2025 AI hype, referencing ChatGPT, GPT-4, Claude, Bard, AI agents, MCP, startups, venture capital, demos, and model hallucinations. ### Source excerpt The AI hype song ## Using Polars in unison with Databricks Unity Catalog DevFeed: [Using Polars in unison with Databricks Unity Catalog]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-05-24T13:57:40Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [data transformation](), [Apache Spark](), [Benchmark]() Tags: [benchmark](), [data-transformation](), [databricks](), [performance](), [spark]() ### AI overview This tutorial explains how to read data from Databricks Unity Catalog directly into Polars dataframes using PyArrow and Deltalake, avoiding an intermediate PySpark dataframe. It also presents a TPC-H benchmark comparing Polars with PySpark or Spark SQL for some data-transformation workloads, with reported potential performance improvements and cost savings for certain data sizes. ### Source excerpt The advent of Polars is not surprising given the performance it delivers. ## How to Test Databricks Model Deployments Locally DevFeed: [How to Test Databricks Model Deployments Locally]() Original publisher: [Read original article]() Author: Mehmet Acikgoz Published: 2025-05-01T16:56:46Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [model-serving](), [databricks](), [DevOps](), [Deployment](), [Testing](), [SDK](), [Command-line interface]() Tags: [cli](), [databricks](), [deployment](), [devops](), [ml](), [model-deployment](), [model-serving](), [sdk](), [testing]() ### AI overview This tutorial explains how machine learning teams can test Databricks model-serving deployments locally before deploying them remotely. It covers downloading a pyfunc model artifact tracked in MLflow, deploying the endpoint locally, and testing it with the Iris-Species dataset. ### Source excerpt Test Databricks model deployments locally ## The Reality of Implementing AI in Corporate Environments DevFeed: [The Reality of Implementing AI in Corporate Environments]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-02-05T13:25:27Z Content type: opinion Language: en Sources: [MarvelousMLOps]() Topics: [Artificial Intelligence](), [implementation](), [Development]() Tags: [ai](), [business](), [business-value](), [news](), [technical](), [technology]() ### AI overview This opinion article examines the gap between AI hype and the practical realities of implementing AI in enterprise environments. It discusses technical debt, regulatory burdens, knowledge gaps, herd effects, and the challenge of converting AI advances into business value. ### Source excerpt The AI Bubble: Hype, herd mentality, and harsh realities ## Navigating Databricks developer tools DevFeed: [Navigating Databricks developer tools]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-02-01T14:00:18Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [Tooling](), [Command-line interface](), [VS Code Extension](), [Python](), [Security]() Tags: [cli](), [databricks](), [developer-tools](), [homebrew](), [python](), [security](), [vs-code]() ### AI overview This tutorial examines Databricks developer tools for working outside the Databricks environment, focusing on the Databricks CLI, Databricks Connect, and the Databricks VS Code Extension. It discusses installation, authentication, configuration evaluation order, local execution of Spark code, deployment, and development and debugging considerations. ### Source excerpt Developing on Databricks outside of Databricks environment is challenging, and there are 4 main developer tools Databricks provides: