Becoming a Machine Learning Engineer | Step 3: Pick Your Tool

As engineers, we spend our careers learning to use new and better tools so that we can build products that bring real value. The tools we use can change often, but they all serve a function and have use cases. Even though machine learning in its modern form is very new, as an industry goes, there is a wide variety of tools at an engineer’s disposal. At the end of this article, you should have a greater understanding of some available tools and whether any of them is right for you.

Main Tools

WEKA | Waikato Environment for Knowledge Analysis

WEKA is a modern machine learning workbench with many great features every ML engineer will need to explore data and apply algorithms. All of this great functionality without having to write a single line of code. Whether you are a programmer or not, I recommend trying out different problems with WEKA.

I also use it a lot when familiarizing myself with new data sets, which should be in everyone's problem-solving process (Becoming a Machine Learning Engineer | Step 2: Pick a process). The built-in algorithms, Graphing tools, easy data import, and the easy to use GUI allow me to explore data much faster than writing a quick script in python.