Serverless ML

Travis chat

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).

🦾 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: 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…

The Road to a Serverless ML Pipeline in Production — Part II

Towards Data Science

In part 1, I explained the process we went through in creating a fully-automated MLOps architecture. In this post, I'll show you the solution we ended up, including code examples of how to implement t...

The Road to a Serverless ML Pipeline in Production — Part I

Towards Data Science

How we managed to run different types of models (our own Python models), each of which with several live versions in production - all in a serverless environment

SageMaker Serverless Inference Is Now Generally Available

Towards Data Science

Exploring The Latest SageMaker Inference Option Continue reading on Towards 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.

The Serverless Framework — Streamlined

Level Up Coding

How I’ve eliminated the pain points I’ve had over the years with the Serverless Framework for my Typescript applications deployed on AWS.

A Brief Introduction to Serverless Computing

Towards Data Science

Over the last decade or two, cloud computing has come to dominate many of the skills and processes needed to develop ‘modern’ software. This is increasingly true for adjacent fields too, including…

The Myths and Misconceptions About Serverless

Better Programming

The demand for serverless computing solutions is growing. But before we go any further, let’s try to explain what this relatively new phenomenon is. The name serverless is pretty contradictory. No…

Serverless Deployment of Machine Learning Models on AWS Lambda

Towards Data Science

In my previous guide, we explored the concepts and methods in deploying machine learning model on AWS Elastic Beanstalk. Despite being largely automated, services like AWS Elastic Beanstalk still…

Models as Serverless Functions

Towards Data Science

I recently published Chapter 3 of my book-in-progress on leanpub. The goal with this chapter is to empower data scientists to leverage managed services to deploy models to production and own more of…

Serverless machine learning architecture on leading cloud platforms

Towards Data Science

The cloud platforms themselves have various services which can be mixed and matched to satisfy the need of any business case with the allocated budget. Here I am going to pick a generic example…

Part-4 Serverless

Analytics Vidhya

We discussed several things in part 1, 2 and 3 of this series. How to make cognito user pools, Lambda functions to interact with these user pools, executing these lambda function behind API gateway…

Serverless ML. Is this the cheapest way to host Machine Learning models on AWS?

Towards Data Science

I recently made a video on using vast.ai for inexpensively building Machine Learning models. This is useful for someone like me, who wants to build a model without a budget. Using vast.ai (I’m not…

Serverless GPU-Powered Hosting of Machine Learning Models

Towards Data Science

With the rise of MLOps in recent years, running machine learning models for inference tasks has become much easier. Depending on the use case, appropriately optimized deep learning models can even…

Does Serverless Have Servers?

ByteByteGo Newsletter

Serverless computing refers to the paradigm of building and running applications without managing the underlying servers. With serverless, the cloud provider abstracts away the servers, runtimes, scal...

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…

Introduction To Serverless Computing in Amazon Web Services

Javarevisited

Serverless Computing is an execution model in cloud computing in which the service provider allocates all the resources required by the server on demand and takes care of it on behalf of the customer....

Pull your ML model out of your Server: The Database Solution

Towards Data Science

When to place your model in your database, how to do it, and why Continue reading on Towards Data Science

Develop and Deploy Serverless Event-Driven Machine Learning Applications

Towards Data Science

How to create application programming interfaces (APIs) hosting machine learning models using Zappa, Python, Flask, and AWS Continue reading on Towards Data Science

The Solution Architect’s Guide to Serverless

Better Programming

You’ve undoubtedly heard about serverless and read about the benefits. But implementing serverless into your app doesn’t have to be all or nothing. For years I’ve been proud to say I build 100% serve...

Building Serverless Applications That Scale The Perfect Amount

Better Programming

Designing for the right amount of scale is a huge architectural task. With serverless part of it is handled for you, but for some of it… Continue reading on Better Programming