Daeploy
@daeploy
Daeploy is an #OpenSource software that helps you to create APIs out of your existing #python code and deploy it as microservices.
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We have taken another step in making Daeploy more accessible to data scientists and made it #OpenSource. Read the post from our product manager @ArashToyser daeploy.com/2021/12/13/dae…
Saurav Dhungana writes that "just like we don’t expect a doctor to know every medical procedure, we shouldn’t expect someone to master everything in the AI either." For the extra mile with the software deployment skills, Daeploy is here to help. 😉 hubs.ly/Q011P7660
💡Managing users in Daeploy is now easier! With single command lines it’s possible to create, manage and remove users. Learn more in our docs: hubs.ly/H0-NlZl0 #Python #AI
Daeploy was built to be simple and straightforward. Don't believe it? Check this video to see how it's possible to go from model to deployed service in 10 minutes: youtube.com/watch?v=-dQwKU…
youtube.com
YouTube
Daeploy Demo: How to deploy a prediction service with Python
That's why we chose simplicity as a core design value for @daeploy. www-infoworld-com.cdn.ampproject.org/c/s/www.infowo…
Here is an example from today where I used Daeploy to deploy the Easy pour algorithm in production in the absence of internet at harsh environment of a foundry.
This means I can build my application locally or using CI/CD pipeline and automatically deploy at the target node (given VPN access is available).
We just released Daeploy version 1.1.0! 🥳 My favorite feature is that I can deploy any docker image I have locally on a target node. This is especially useful in industrial settings when the target machine doesn't have access to the internet. daeploy.com
Interesting article proposing a framework to tackle the common challenges in making #MachineLearning activities more efficient. Daeploy fits right into the model serving process, by enabling the deployment of models as microservices in a simple way. hubs.ly/H0T3j130
towardsdatascience.com
Improving Model Management Practices for Machine Learning Teams | Towards Data Science
How to identify issues in making machine learning activities more efficient through model management capabilities
One of the most important things for freelance data scientists, especially those working in industries, is having a set of reliable and flexible tools. Daeploy let them easily deploy #MachineLearning models into production and scale-up pilot projects. hubs.ly/H0T3jS10
And when your PoC is good to go, Daeploy is there to help you deploy it as a microservice. Learn more and get started for free: daeploy.com
A good way to break down a machine learning project: 1. Proof of Concept phase 2. Production phase First, focus on making sure you can solve the problem with a small, working prototype. If feasible, enhance your solution, scale it, and build a replicable data pipeline.
Have you got 10 minutes? Then don't miss our demo video that shows how to deploy a #MachineLearning prediction service directly from a Jupyter Notebook. Check it out: hubs.ly/H0T221c0
youtube.com
YouTube
Daeploy Demo: How to deploy a prediction service with Python
Top programming language for data science: Python still rules, followed by SQL zdnet.com/article/top-pr…
It is common that data scientists or ML engineers are tasked to convert their models and algorithms to applications. In the blog, we explain how Daeploy was created to solve their pain in this process and keep focus on their craft: hubs.ly/H0SxQ0p0
Did you know that our Getting Started Guide gives you access to a free 12-hour trial of the manager? Follow our tutorial and experiment with turning your #Python code into microservices: hubs.ly/H0S1MSK0
daeploy.com
Getting started with Daeploy - Daeploy
Tutorial to configure, create, and deploy your first service using Daeploy, a tool to make Machine Learning software deployment easy.
How long does it take do deploy a prediction service? Our product manager, @ArashToyser, uses the classic #MachineLearning Iris Flower example to show how it can be done with Daeploy in 10 minutes. Check it out: hubs.ly/H0R5Zx70
youtube.com
YouTube
Daeploy Demo: How to deploy a prediction service with Python
You can deploy to any machine or virtual machine! But since the only requirement is #Docker and access to Docker daemon, Daeploy is specially suitable for on-prem deployment. Learn more and get started today: hubs.ly/H0QGK5p0
Daeploy works without internet access! You need to download the Daeploy Manager image and save it as a tar file using Docker save. The tar file can then be transferred to the machine/server using SCP or a similar tool. Learn more and get started: hubs.ly/H0Qb5vY0
Using the Daeploy Manager goes smoothly if the server can run #Docker. Daeploy's only requirement is access to the Docker daemon. Learn more and get started: hubs.ly/H0PW-nQ0
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