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...

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…

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...

Serverless

Full Stack Python

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

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 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...

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…

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 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…

What Wikipedia Can’t Tell You About Serverless

Better Programming

Serverless is a hot technology in which applications are executed on-demand in response to specific triggers. The code that runs in this way is called FaaS.

Serverless Vector Search with txtai

Towards Data Science

Build a serverless txtai instance with Kubernetes and Knative Photo by José Ramos on Unsplash Serverless application development is a popular way for developers to skip over the complexity of servers...