Serverless ML

Serverless ML refers to a cloud computing paradigm that allows developers to deploy machine learning models without the need to manage server infrastructure. This approach simplifies the deployment process by automatically scaling resources based on demand, enabling models to run continuously or intermittently as needed. Serverless ML is particularly beneficial for applications with fluctuating workloads, as it eliminates the complexities of provisioning and maintaining servers. By leveraging serverless architectures, organizations can focus on building and optimizing their models while ensuring they are readily accessible for inference, ultimately enhancing efficiency and reducing operational costs.

🦾 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...

Serverless ML

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…

Building End-to-End Machine Learning (ML) Lineage for Serverless ML Systems

Towards AI

Member-only story Building End-to-End Machine Learning (ML) Lineage for Serverless ML Systems A practical guide on ML lineage fundamentals and MLOps workflow implementation Kuriko Iwai 21 min read · J...

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...

Accelerating AI: How Serverless GPUs Are Revolutionizing Model Training

Level Up Coding

M achine Learning models require extensive computational power, especially when we’re working with big data and/or with complex models, like (very) Deep Neural Networks. Until recently, the typical so...

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…

Amazon SageMaker Serverless MLflow: The Future of Simple, Scalable MLOps

Python in Plain English

How AWS is transforming machine learning workflows with a fully managed, serverless MLflow experience. Photo by Bryan Angelo on Unsplash Machine learning is powerful… but let’s be honest: managing ML...

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…...