# machine-learning-platform Published articles for machine-learning-platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Deliveroo Built Its Machine Learning Platform DevFeed: [How Deliveroo Built Its Machine Learning Platform]() Original publisher: [Read original article]() Author: Saikrishna Desaraju Published: 2025-07-02T00:00:00Z Content type: article Language: en Sources: [Deliveroo]() Topics: [Machine Learning & Artificial Intelligence](), [Deployment](), [model-deployment](), [Kubernetes](), [Amazon Web Services](), [distributed-training](), [Inference](), [Python](), [PyTorch](), [Tensorflow]() Tags: [aws](), [deployment](), [distributed-training](), [inference](), [kubernetes](), [machine-learning](), [machine-learning-platform](), [python](), [pytorch](), [rest](), [tensorflow]() ### AI overview Deliveroo describes its machine learning platform for standardizing model-building and deployment workflows. The platform combines open-source tools, in-house components, Kubernetes on AWS EKS, TensorFlow, PyTorch, Metaflow, and Argo; it also supports GPU and distributed training and includes Inferoo for real-time model predictions. ### Source excerpt Enter Deliveroo's ML Platform For the past three years, we have been building Deliveroo's Machine Learning Platform, or the ML Platform as we like to call it. The ML Platform boosts our model-building and deployment capabilities by standardising ML workflows, streamlining the end-to-end development process and simplifying model deployment. Besides saving software engineering effort through centralising tooling, the ML Platform also reduces the time that our ML engineers spend on infrastructure tasks. As a result, our ML engineers can now iterate their ML models 2-3x faster than before. What makes our ML Platform tick? At the core of our ML Platform lies a carefully curated tech stack - an integrated suite of infrastructure tools, services, and libraries. It blends robust open source technologies with purpose-built, in-house components. Key open source tools include Kubernetes, Argo, and Metaflow, all seamlessly connected with leading ML frameworks like TensorFlow and PyTorch. We choose mature, community-driven solutions and actively contribute back where we can. This entire ecosystem is powered by AWS, running on EKS, and anchored by our data warehouse. In the sections that follow, we'll take a closer look at the key components that drive our ML Platform. Metaflow To give ML engineers seamless access to scalable compute on Kubernetes, we use Metaflow, a powerful Python library that helps break down complex model-building workflows into smaller, manageable jobs. These jobs are orchestrated by Argo, one of the backbone tools in our infrastructure. One of Metaflow's biggest strengths is its flexibility. It allows engineers to move effortlessly between local development, staging on Kubernetes, and production deployment--helping teams iterate quickly as they experiment and scale. Recently, we added support for GPUs and distributed training, enabling faster training times and the ability to work with much larger datasets. Inferoo For models that require real-time predictio ## Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution DevFeed: [Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution]() Original publisher: [Read original article]() Author: Rich Papalia Published: 2023-08-30T21:20:29Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [Machine Learning & Artificial Intelligence](), [Open Source](), [Development](), [Amazon SageMaker AI]() Tags: [aws](), [configuration-management](), [feature-store](), [lifecycle](), [machine-learning](), [machine-learning-platform](), [open-source](), [pipelines](), [registry]() ### AI overview Glassdoor describes its newly open-sourced ML Registry, a centralized service for managing machine-learning artifacts and related metadata. The registry is intended to provide a single source of truth and consistent access across teams and applications, integrating with other tools and services. ### Source excerpt Photo by Ryunosuke Kikuno on UnsplashGlassdoor's Machine Learning Engineering and Platform Team As part of Glassdoor's journey to becoming an ML-driven company, we've created an exceptional Machine Learning Engineering and Platform team, (see our blog about building this team here) dedicated to developing the foundational infrastructure for all of our ML initiatives. We've strategically built our ML platform by utilizing a combination of buying, building, and adopting from existing open-source solutions. This approach has allowed us to leverage the best of all worlds. Our team has had the opportunity to build numerous tools from scratch, including data pipelines, human-in-the-loop tools, and more. We've also built on top of existing tools like AWS Sagemaker to incorporate powerful in-house customizations into our feature store. We have more exciting plans in flight, such as building a new recommendations platform, further open-source contributions from our platform, and continuing to build on the foundation which we've established. Today, we'll delve into our newly open-sourced ML Registry. What is an ML Registry? The ML lifecycle encompasses far more than just model development. Once a model is built, many questions arise: Where does it reside? How do we access it? What if updates or versioning are needed? Where can we store the metadata describing the model? And what about other non-model artifacts? How can we efficiently manage all of this? Enter the ML Registry. Glassdoor's ML Registry is a centralized management service for ML artifacts and all related metadata. It serves as the single source of truth for all data pertaining to ML, enabling uniform and reliable access to this data across diverse teams and applications. It seamlessly integrates with other tools and services and provides robust, feature-rich functionality. Choosing Between Buying and Building, What Differentiates Our ML Registry? While several paid and open-source model registries already exist, ## 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