Serverless ML

🦾 Serverless ML Execution

TheSequence

📝 Editorial One of the most challenging aspects of modern ML solutions is to match the right infrastructure for executing a given ML model. Some are executed continuously, while others intermittently...

Towards Data Science

Wrangling data and training models can feel a little… disconnected. In this post I’ll walk through deploying an ML model in the cloud. It’s quick, easy and free (I think).

How to do serverless machine learning with scikit-learn on Google Cloud ML Engine

Towards Data Science

On Google Cloud Platform, Cloud ML Engine provides serverless machine learning, for training, hyperparameter optimization and predictions. Until recently, that was only for TensorFlow. Recently…

Deploying Machine Learning Projects as Serverless APIs

Towards AI

A guide to deploying Production Ready Data Science Projects as Serverless APIs with Azure Functions. Photo by Milad Fakurian on Unsplash Introduction When done right, Machine Learning and Data Science...

Deploying a Serverless Inference Service with Amazon SageMaker Pipelines

Towards Data Science

Deploying some of your ML models into serverless architectures allows you to create scalabale inference services, eliminate operational overhead, and move faster to production. I have published…

Serverless

Full Stack Python

Serverless is an deployment architecture where servers are not explicitly provisioned and code is run based on pre-defined events.

Deploying TFLite model on GCP Serverless

Towards Data Science

Understanding Serverless and other ways of Deployment Let’s first understand what do we mean by serverless because serverless doesn’t mean without a server. An AI model, or any application for that ma...

Serverless ML: Deploying Lightweight Models at Scale

Towards Data Science

Deploying machine learning (ML) models into production can sometimes be something of a stumbling block for Data Science (DS) teams. A common mode of deployment is to find somewhere to host your…

How To Build and Deploy a Serverless Machine Learning App on AWS

Towards Data Science

Have you ever wanted to build a machine learning application with a heavy model on the backend, a React user-friendly interface on the frontend, and a serverless cloud architecture all around so that…...

Machine Learning Model as a Serverless App using Google App Engine

Towards Data Science

So you have built a model and want to deploy it as a serverless app. Let me show you how to do this on Google Cloud Platform (GCP) using Docker Containers, Streamlit, and Google App Engine!

Productionize Machine Learning Models with Serverless Container Services

Towards Data Science

Serverless container architecture is an approach to building and running containerized applications and services without having to manage the underlying infrastructure. In this architecture…

Building a serverless, containerized machine learning model API using AWS Lambda & API Gateway and…

Towards Data Science

Goal of this post is a to set up a serverless infrastructure, managed in code, to serve predictions of a containerized machine learning model via Rest API as simple as: We will make use of Terraform…