Dolly  Yost

Dolly Yost

1622089602

How to Deploy SvelteKit to Vercel

In this video we quickly deploy a SvelteKit project to Vercel. There are minimal steps required to go from local app to hosted deployment in just a couple minutes. The SvelteKit Vercel adapter provides both client and server functionality by taking advantage of Vercel’s serverless functions offering.

#sveltekit #vercel

What is GEEK

Buddha Community

How to Deploy SvelteKit to Vercel
Dolly  Yost

Dolly Yost

1622089602

How to Deploy SvelteKit to Vercel

In this video we quickly deploy a SvelteKit project to Vercel. There are minimal steps required to go from local app to hosted deployment in just a couple minutes. The SvelteKit Vercel adapter provides both client and server functionality by taking advantage of Vercel’s serverless functions offering.

#sveltekit #vercel

Justice  Reilly

Justice Reilly

1595294400

Deploying Machine learning models using Flask on your website

Understanding of Machine Learning using Python (sklearn)
Basics of Flask
Basics of HTML,CSS

#machine-learning #deployment #ml-model-deployment #flask #deploying

Turner  Crona

Turner Crona

1595837400

Automate Deployment to CloudHub using CloudHub Deployer Plugin Jenkins

Introduction

We live in an age, Where DevOps and automation are becoming more and more necessary and important in projects. So uploading packages manually to servers or platforms is not feasible and salable when you work with architecture like micro-services. So to tackle this problem we need to implement Continuous Delivery and Deployment cycle in our project. In this post I will be showing you how to do exactly that with Mule applications.

After creating a basic Mule App, you might be wondering how to automate the process of deploying a Mule App to CloudHub. In this post, I will be introducing a Jenkins plugin(Github Repository) that I published recently that enables this use case.

How it is compared to other solution/tools available with Jenkins:

Mule-Maven plugin - With this approach you are tight coupling you build and deploy process and most of time its not good. And its hard to scale this approach when you have multi environment deployment and many applications to manage. This approach will not work if you just want to do deployment.

This approach will take time and effort to get working automation that meets your project requirement. The CloudHub Deployer plugin itself is built using same API why re-invent the wheel.

What we will accomplish here:

Jenkins release pipeline using both free style and pipeline script that automates your mule application deployment to CloudHub.

Prerequisites:

  1. You will need to have Jenkins instance up and running.
  2. A CloudHub account.
  3. You need to have a already built package to follow along. Since I am not covering CI(Continuous Integration) for mule apps, there are plenty article on internet for that.

#integration #deployment #jenkins #mulesoft #mule #deployment automation #cloudhub #jenkins pipeline #jenkins automation

Michael  Hamill

Michael Hamill

1617331277

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop