# model-deployment Published articles for model-deployment. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Gesture Recognition Based on TFLite DevFeed: [Gesture Recognition Based on TFLite]() Original publisher: [Read original article]() Author: John Lee Published: 2026-04-10T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [TensorFlow Lite](), [Espressif](), [model-deployment](), [Tensorflow](), [Keras](), [On-device AI]() Tags: [ai](), [blog](), [deep-learning](), [deployment](), [development-board](), [edge-ai](), [edge-computing](), [esp-idf](), [espressif](), [how-to](), [inference](), [keras](), [model-deployment](), [model-training](), [tensorflow-lite]() ### AI overview This tutorial demonstrates gesture recognition on Espressif SoCs using TensorFlow Lite Micro. It covers data collection, model training, conversion for TFLite Micro, and deployment with C++ code for model loading, preprocessing, and inference. ### Source excerpt This article demonstrates how to implement gesture recognition using TensorFlow Lite Micro on Espressif SoCs. It covers the complete workflow from data collection and model training to model deployment, showcasing TensorFlow Lite Micro's applications in edge AI. ## 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 ## Hugging Face and FriendliAI partner to supercharge model deployment on the Hub DevFeed: [Hugging Face and FriendliAI partner to supercharge model deployment on the Hub]() Original publisher: [Read original article]() Author: Ahnjae Shin; Soomin Chun; Byung-Gon Chun; Julien Chaumond Published: 2025-01-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [friendliai](), [model-deployment](), [AI Inference](), [hugging face](), [Inference Performance](), [autoscaling](), [GPU](), [Open Source Models & Datasets](), [quantization]() Tags: [ai-inference](), [autoscaling](), [batching](), [deployment](), [friendliai](), [gpu](), [hub](), [hugging-face](), [latency](), [model](), [model-deployment](), [open-source](), [partnerships](), [performance](), [quantization]() ### AI overview Hugging Face and FriendliAI announce an integration that adds FriendliAI Endpoints as a deployment option in the Hugging Face Hub. Developers can deploy open-source or custom generative AI models through FriendliAI's inference infrastructure, including NVIDIA H100 GPUs, continuous batching, quantization, and autoscaling. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Introducing HUGS - Scale your AI with Open Models DevFeed: [Introducing HUGS - Scale your AI with Open Models]() Original publisher: [Read original article]() Author: Philipp Schmid; Jeff Boudier; Alvaro Bartolome; Simon Pagezy; Violette Published: 2024-10-23T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [model-deployment](), [tgi](), [Inference](), [Transformers](), [Open Source](), [Generative AI](), [Large Language Model](), [GPU](), [Microservices](), [API](), [Hardware]() Tags: [accelerate](), [accelerators](), [ai](), [ai-accelerator](), [amd](), [announcement](), [api](), [artificial-intelligence](), [aws](), [aws-inferentia](), [azure](), [digitalocean](), [enterprise](), [gcp](), [gpu](), [inference](), [llm](), [microservices](), [model-deployment](), [models](), [multimodal](), [open-source](), [partnerships](), [performance](), [technologies](), [text-generation](), [tgi](), [transformers]() ### AI overview Hugging Face introduces HUGS, optimized zero-configuration inference microservices for deploying open models in an organization's own infrastructure. Built on Hugging Face technologies including Text Generation Inference and Transformers, HUGS targets efficient, hardware-optimized deployment across NVIDIA and AMD GPUs, with AWS Inferentia and Google TPU support planned. It provides an OpenAI-compatible API and is designed to reduce deployment complexity and time for AI applications. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Announcing early access to Chainguard's CUDA Optimized Images DevFeed: [Announcing early access to Chainguard's CUDA Optimized Images]() Original publisher: [Read original article]() Published: 2024-04-25T00:00:00Z Content type: news Language: en Sources: [Chainguard: Unchained]() Topics: [CUDA](), [container images](), [Containers](), [GPU](), [PyTorch](), [Security](), [Vulnerabilities](), [model-deployment](), [AI Development]() Tags: [ai](), [ai-and-cybersecurity](), [ai-development](), [ai-software-supply-chain](), [ai-software-supply-chain-security](), [artificial-intelligence](), [chainguard](), [chainguard-images](), [chainguard-pytorch-image](), [container-images](), [containers](), [cuda](), [cves](), [deployment](), [gpu](), [image-cves](), [machine-learning](), [ml](), [ml-and-cybersecurity](), [ml-software-supply-chain](), [ml-software-supply-chain-security](), [model-deployment](), [nemo](), [nvidia](), [pytorch](), [security]() ### AI overview Chainguard announces early access to CUDA Optimized Images, designed to secure and simplify the deployment and management of NVIDIA accelerated AI applications in containers. The images target model deployment tampering, container vulnerabilities, large image sizes, driver and CUDA compatibility issues, dependency mismatches, and GPU debugging challenges. ### Source excerpt Accelerate AI development with Chainguard's CUDA Optimized Images. Secure, streamlined NVIDIA deployments -- join our Early Access Program! ## Leveraging the Plain Old Python Function DevFeed: [Leveraging the Plain Old Python Function]() Original publisher: [Read original article]() Published: 2022-12-14T09:00:00Z Content type: article Language: en Sources: [Stitch Fix]() Topics: [Python](), [MLOps](), [Data Science](), [Framework](), [Deployment]() Tags: [apis](), [blog-post](), [dsl](), [functional](), [microservices](), [mlops](), [model-deployment](), [python]() ### AI overview Stitch Fix describes a function-first approach to data science APIs and MLOps. Instead of building custom model-deployment mechanisms, microservices, and tightly coupled data transformations, data scientists can use plain Python functions backed by platform infrastructure. ### Source excerpt The role of the full-stack-data-scientist is not what it once was. With the advent of more powerful tooling, new industry standards in MLOps, and greater investment in platforms, the day-to-day of a data scientist has changed significantly at Stitch Fix. The difference, however, is subtle. The structure of their job remains the same - engineers still do not write ETLs and data scientists function as generalists, but they now have to think on a higher level. Their job is constantly getting more and more complex--the business needs are in flux and the infrastructure they use is more powerful than it ever was. The old strategy of cobbling together complex systems will only end in stressed-out data scientists with too much infrastructure on their plate. To avoid this cycle of complexity, Stitch Fix invests in a platform team to innovate new ways of supporting a data scientist's engineering needs. Rather than constructing custom model-deployment mechanisms, building microservices from the ground up, and managing highly interdependent chains of data transformations, data scientists at Stitch Fix can leverage powerful infrastructure by constructing plain old Python functions to represent their needs. In this blog post we're going to take a different approach than usual. Rather than digging into a specific piece of technology, we'll present our philosophy of functions for data science APIs and back it up with some motivating examples. We'll explain the power of functions as a DSL, share some successes we've had using functional interfaces to build our MLOps stack, and connect our approach with external, open-source frameworks that the industry is beginning to adopt. Our goal is to convince you that a function-first approach will enable data practitioners to do more while doing less. The functional approach allows them to plug into the business in a scalable manner while avoiding the complexity of managing infrastructure and architectural decisions. On Functions and Functiona ## Discovering the systematic errors made by machine learning models DevFeed: [Discovering the systematic errors made by machine learning models]() Original publisher: [Read original article]() Author: Sabri Eyuboglu, Maya Varma, Khaled Saab, Jared Dunnmon, James Zou and Chris Ré Published: 2022-04-07T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [Machine Learning & Artificial Intelligence](), [Embeddings](), [data](), [model-deployment](), [debug](), [Optimization]() Tags: [data](), [datasets](), [debug](), [embeddings](), [evaluation](), [machine-learning](), [model](), [model-deployment](), [optimization](), [validation]() ### AI overview This article introduces Domino, a method for discovering systematic errors that machine learning models make on coherent, underperforming slices of validation data. It explains how slice awareness supports model evaluation, deployment decisions, debugging, robustness improvements, and safety and fairness analysis, including through cross-modal embeddings. ### Source excerpt Discovering systematic errors with cross-modal embeddings In this blog post, we introduce Domino, a new approach for discovering systematic errors made by machine learning models. We also discuss a framework for quantitatively evaluating methods like Domino. Links: 📄 Paper (ICLR 2022) 🌍 Longer Walkthrough 💻 GitHub 📘 Docs 📒 Google Colab Machine learning models that achieve high overall accuracy often make systematic errors on coherent slices of validation data. What is a slice? A slice is a set of data samples that share a common characteristic. As an example, in large image datasets, photos of vintage cars comprise a slice (i.e. all images in the slice share a common subject). The term slice has a number of synonyms that you might be more familiar with (e.g. subgroup, subpopulation, stratum). These terms are largely interchangeable, but we'll stick with "slice" throughout this post. We say that a model underperforms on a slice if performance on the data samples in the slice is significantly worse than its overall performance. The search for underperforming slices is a critical, but often overlooked, part of model evaluation. When practitioners are aware of the slices on which their models underperform, they can make more informed decisions around model deployment. This is particularly important in safety-critical settings like medicine: a diagnostic model that underperforms on younger patients should likely not be deployed at a pediatric hospital. Slice awareness can also help practitioners debug and improve models: after an underperforming slice is identified, we can improve model robustness by either updating the training dataset or using robust optimization techniques (e.g. Sohoni et al., 2020; Sagawa et al., 2020). Deploying models that underperform on critical data slices may have significant safety or fairness consequences. For example, models trained to detect collapsed lungs in chest X-rays have been shown to make predictions based on the presence of chest d ## An Overview of Machine Learning Operations DevFeed: [An Overview of Machine Learning Operations]() Original publisher: [Read original article]() Author: Erin Wolpert Published: 2021-11-30T20:00:00Z Content type: tutorial Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [MLOps](), [CI/CD](), [data-engineering](), [model-deployment](), [Model Development](), [Monitoring]() Tags: [ci-cd](), [data-engineering](), [data-science](), [development](), [lifecycle](), [mlops](), [model-deployment](), [monitoring](), [platform]() ### AI overview An overview of MLOps as a discipline for standardizing and streamlining the machine learning lifecycle. The article explains how MLOps applies CI/CD practices, automation, and monitoring to reduce maintenance burden, complexity, and technical debt in production ML systems. ### Source excerpt Building Mature Machine Learning Production Systems through MLOps