# ml-engineering Published articles for ml-engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NVIDIA Nemotron Nano 2 VL 12B: Architecture and Improvements for Multimodal Reasoning DevFeed: [NVIDIA Nemotron Nano 2 VL 12B: Architecture and Improvements for Multimodal Reasoning]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-11-22T14:58:39Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Nemotron](), [multimodal](), [Nvidia](), [context window](), [long-context](), [Benchmark](), [Mamba](), [Transformer](), [AI Models]() Tags: [ai-ml](), [architecture](), [benchmark](), [context-window](), [long-context](), [mamba](), [ml-engineering](), [multimodal](), [nemotron](), [nvidia]() ### AI overview This article examines NVIDIA's Nemotron Nano 2 VL 12B, an open small vision-language model designed for document understanding, long-video comprehension, and multimodal reasoning. It discusses the model's architecture, encoders, training and inference improvements, reasoning modes, and reported benchmark performance, including a 128k-token context window. ### Source excerpt What makes NVIDIA Nemotron Nano 2 VL a breakthrough in small, fast, long-context visual reasoning. ## How to build a Machine Learning portfolio in 2024? DevFeed: [How to build a Machine Learning portfolio in 2024?]() Original publisher: [Read original article]() Author: Meri Nova Published: 2024-10-16T19:14:35Z Content type: tutorial Language: en Sources: [Meri Nova]() Topics: [Machine Learning & Artificial Intelligence](), [AI Engineering](), [Software Engineering](), [clean-code]() Tags: [ai-engineering](), [how-to](), [machine-learning](), [ml-engineering](), [practical](), [project](), [real-world]() ### AI overview A practical guide to building a machine learning portfolio around a focused area of expertise. It presents the STEP framework: solve real-world problems, use relevant tools, follow strong engineering practices, and publish documented impact. ### Source excerpt Learn about the 4 essential components of an outstanding ML portfolio and land your dream job. ## Building Glassdoor's Machine Learning Platform and Engineering Team DevFeed: [Building Glassdoor's Machine Learning Platform and Engineering Team]() Original publisher: [Read original article]() Author: Malathi Sankar Published: 2022-08-01T07:19:34Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [MLOps](), [Machine Learning & Artificial Intelligence](), [Data Infrastructure](), [DevOps](), [Monitoring](), [airflow](), [debug](), [Provisioning]() Tags: [airflow](), [data](), [data-infrastructure](), [debugging](), [devops](), [engineering](), [glassdoor](), [infrastructure](), [machine-learning](), [machine-learning-platform](), [ml-engineering](), [mlops](), [monitoring](), [organization]() ### AI overview This article describes how Glassdoor developed and scaled its machine learning platform and engineering team. It covers the organization's early machine learning systems, deployment and infrastructure challenges, adoption of MLOps practices, and the use of combined open-source, commercial, and proprietary tools to orchestrate its machine learning pipeline. ### Source excerpt In this post, I would like to share how Glassdoor built and scaled its ML platform and engineering team and what we learned along the way. Historic ML journey at Glassdoor Ten years ago, machine learning at Glassdoor started with a straightforward logistic regression model, which was used to power our job recommendations. Today machine learning is ubiquitous. It is used across most of our products -- search, ads, reviews, salaries, interviews, benefits, and Fishbowl post recommendations, to name a few. Data quality, model innovations, hardware advancements, data infrastructure, resource investments, and tools propelled this growth. Until 2021, our machine learning team was mostly comprised of scientists who were experts in math and statistics. They worked with data engineers to prepare data and build optimized models to solve problems. Batch deployments were done in Airflow by the scientists, while project engineers carried out the online model deployments. Infrastructure provisioning was left to the DevOps team. This organizational structure was less than ideal, however, and created many challenges. Communication gaps and resource misallocations made deployments tedious and time-consuming. Monitoring, observing, maintaining, and debugging models were difficult. Machine Learning's technical debt grew rapidly and had a negative impact on Glassdoor's operations. Adoption of MLOps framework: To deal with the above challenges, we conceptualized real-time machine learning as primarily an engineering and infrastructure problem. As Scully et al. have discussed in this paper [1], only a small fraction of a real-world ML system is composed of ML code (illustrated by the black box in Figure 1), while the surrounding infrastructure is vast and complex. By adopting a set of DevOps and Engineering best practices known as MLOps, we were able to effectively manage this complexity. As previously discussed in our blog, our initial approach comprised a mix of open source tools, commer