How to succeed as a machine learning engineer

Machine Learning is a vehicle that uses data as a fuel to achieve the goal of Artificial Intelligence. This goal manifests itself in various forms depending on what specific outcome you want to achieve.

As a machine learning engineer, you'll find yourself building products that need to work in the wild. Here are 7 skills will help you succeed:

1. Need to be able to tell others what you did

Explaining your work is critical when you're building an AI product. You'll be working with teammates who work closely with customers. You need to empower them with clean explanations

2. Need to know Python

Python has become the language of AI. You can survive without it, but you'll have a hard time. People build libraries and tools in Python. It will help you leverage their work to build products quickly.

3. Being okay with wild goose chases

You need to go on frequent wild goose chases. It's not an easy task. Data is usually not clean. And the goals are not well defined either. You should be disciplined about it.

4. Being able to translate

You should be able to listen to a problem in plain english and translate it. And figure out what algorithmic framework to use while analyzing the data. 

5. Need to know ML algorithms

This one is obvious, but it's important to know the categories and hierarchies here. You'll encounter different problems and different types of data. Based on the situation, you need to quickly make decisions on what to use.

6. Need to know statistics

There are no certainties in this world. Only probabilities. You should get comfortable living in the world of probabilities. Learn how to design and develop systems based on it.

7. Need to have domain knowledge

The domain here refers to the problem. Are you solving a problem in the physical world? Or finance? Or cybersecurity? You need to know what frameworks are applicable to what type of data.