Model Serving Techniques - Learn Data Science with Travis

Model-Serving-Techniques

Model serving techniques refer to the methods and frameworks used to deploy machine learning models so they can be accessed and utilized in real-time applications. This process is crucial for integrating models into production environments, allowing them to provide predictions or insights based on incoming data. Various techniques exist, including using dedicated serving frameworks like TensorFlow Serving and TorchServe, as well as web frameworks such as Flask and FastAPI. The choice of technique often depends on the specific requirements of the application, such as latency, scalability, and the complexity of the model being served.

🍮 Edge#147: MLOPs – Model Serving

TheSequence

In this issue: we explain what model serving is; we explore the TensorFlow serving paper; we cover TorchServe, a super simple serving framework for PyTorch. 💡 ML Concept of the Day: Model Serving Con...

🌀Edge#12: The challenges of Model Serving~

TheSequence

In this issue: we explain the concept of model serving; we review a paper in which Google Research outlined the architecture of a serving pipeline for TensorFlow models; we discuss MLflow, one of the ...

Serving ML Models with TorchServe

Towards Data Science

A complete end-to-end example of serving an ML model for image classification task Image by author Motivation This post will walk you through a process of serving your deep learning Torch model with ...

Stateful model serving: how we accelerate inference using ONNX Runtime

Towards Data Science

Stateless model serving is what one usually thinks about when using a machine-learned model in production. For instance, a web application handling live traffic can call out to a model server from…

101 For Serving ML Models

Pratik’s Pakodas 🍿

Learn to write robust APIs Me at Spiti Valley in Himachal Pradesh → ML in production series Productionizing NLP Models 10 Useful ML Practices For Python Developers Serving ML Models My love for unders...

Model serving architectures on Databricks

Marvelous MLOps Substack

Many different components are required to bring machine learning models to production. I believe that machine learning teams should aim to simplify the architecture and minimize the amount of tools th...

Serving TensorFlow models with TensorFlow Serving

Towards Data Science

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments.

Several Ways for Machine Learning Model Serving (Model as a Service)

Towards AI

No matter how well you build a model, no one knows it if you cannot ship model. However, lots of data scientists want to focus on model building and skipping the rest of the stuff such as data…

Deploying PyTorch Models with Nvidia Triton Inference Server

Towards Data Science

Machine Learning’s (ML) value is truly recognized in real-world applications when we arrive at Model Hosting and Inference . It’s hard to productionize ML workloads if you don’t have a highly performa...

Serving a model using MLflow

Analytics Vidhya

The mlflow models serve command stops as soon as you press Ctrl+C or exit the terminal. If you want the model to be up and running, you need to create a systemd service for it. Go into the…

Scaling Machine Learning models using Tensorflow Serving & Kubernetes

Towards Data Science

Tensorflow serving is an amazing tool to put your models into production from handling requests to effectively using GPU for multiple models. The problem arises when the number of requests increases…

Serve hundreds to thousands of ML models — architectures from industry

Towards Data Science

When you only have one or two models to deploy, you can simply put your models in a serving framework and deploy your models on a couple of instances/containers. However, if your ML use cases grow or…...