5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible. If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done.
If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.
Building good Machine Learning applications is like making Michelin-style dishes. Having a well organized and managed kitchen is critical, but there are too many options to choose from. In this article, I highlight the tools I found useful in delivering professional projects and share a few thoughts and alternatives. Like any tooling discussion, the list is not exhaustive. I try to focus on the most useful and simplest tools.
Disclaimer: This post is not endorsed or sponsored. I use the term Data Science and ML interchangeably.
“How do I build good Machine Learning applications?”
This question came up many times and in various forms during chats with aspiring data scientists in schools, professionals who are looking to switch, and team managers.
There are many aspects of delivering a professional data science project. Like many others, I like to use the analogy of cooking in a kitchen: there is the ingredient (data), the recipe (design), the process of cooking (well, your unique approach), and finally, the actual kitchen (tools).
So, this article walks through my kitchen. It highlights the most useful tools to design, develop, and deploy full-stack Machine Learning applications — solutions that integrate with systems or serve human users in Production environments.
We live in a golden age. If you search “ML tools” in Google or ask a consultant, you are likely to get something like this:
In this article we are going to compare three most popular machine learning projects for you.
In this Python tutorial, you'll learn how to use GitHub Actions to deploy a FastAPI project to Heroku. We’ll be using GitHub Actions to configure a CI/CD pipeline. And, FastAPI to build our hypothetical REST API. This article shows how to use GitHub Actions in tandem with Heroku while maintaining best practices.
Deploy to Heroku with GitHub Actions. This article goes over how to deploy to Heroku using the GitHub Actions workflow. Create a Heroku app and save the app’s name and email associated with your account.
Create and Deploy your first Deep Learning app! Learn how to deploy our PyTorch model with Flask and Heroku. We create a simple Flask app with a REST API that returns the result as json data, and then we deploy it to Heroku. As an example PytTorch app we do digit classification, and at the end I show you how I draw my own digits and then predict it with our live running app.
Free .net core hosting on Heroku through Docker and GitHub. Guide for startups on deploying their project! Code samples included.Heroku is a cloud-based PaaS platform that supports a variety of programming languages (C# was not officially supported at the time of this article, but that doesn’t stop us) and is based on a managed container system for deployment and launching applications. Heroku provides DNS * .herokuapp.com.