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.

Overwhelming Possibilities

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:

1. The Fridge: Databases

2. The Countertop: Deployment Pipeline Tools

3. the iPad: Exploration Tools

4. The Knives: ML Frameworks

5. The Stove: Experimentation Management

#github #heroku #pycharm #pytorch