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Essential Skills For Machine Learning Engineers

Building a Machine Learning Engineer

Photo by ThisisEngineering RAEng on Unsplash
Photo by ThisisEngineering RAEng on Unsplash

Do you enjoy creating software but are extremely intrigued by Data Science? If so, you may want to consider the role of a Machine Learning Engineer. Machine Learning engineers sit at the intersection of Software Engineering and Data Science – meaning you’ll need both skills if you really want to excel.

The focus of Data Scientists is to transform disparate data into actionable insights. On the other hand, the Machine Learning Engineer focuses on developing working software that makes use of the data as well as automating predictive models.

Overview Of Data Related Roles

Here’s a summary of the skills required:

Software Engineering

Computer Science fundamentals are vital for the Machine Learning engineer; A good grasp of Data Structures & Algorithms such as Multi-dimensional arrays, arrays, stacks, queues, trees, etc. ML engineers should also be able to write algorithms that are capable of searching, sorting, and optimizing code. To further add, an understanding of computability, complexity, and computer architecture are all necessary.

Since the final output from a Machine Learning engineer is typically deliverable software, ML engineers must have a good idea of how each of the different pieces of software works and communicates to build suitable interfaces for your component.

Data Science

Data Scientists typically have a heavy reliance on programming languages such as Python, R, SQL, Java, etc. They also have a sound foundational base in probability and statistics – Topics include;

Note: This is by no means an exhaustive list. For more read my article, Courses to Learn Data Science in 2021

Also, ML engineers should have adept data modeling and evaluation skills. Data modeling is the process of training a learning algorithm to predict the labels given a set of features.

The goal of modeling is to identify useful patterns that best allow the model to generalize to new unseen instances – here is where evaluation comes into play. At the beginning of a Machine Learning project, the most appropriate evaluation metric for the task will be used to determine how well the algorithm has performed.

Machine Learning

Many of the widely used Machine Learning algorithms can be implemented through third-party libraries such as Scikit-Learn, Keras, TensorFlow, PyTorch, MLlib, etc. However, applying these algorithms effectively includes selecting a model that is suitable for the problem at hand, an optimization method, and understanding the effect of hyperparameters on learning.

Additionally, ML engineers should be adept at hyperparameter tuning. A hyperparameter is a parameter value that is used to control the learning process, therefore hyperparameter tuning may be described as the problem of selecting a set of optimal hyperparameters for a learning algorithm.

Some other tools ML Engineers may be required to know (depending on the company they work for) include:

  • Spark & Hadoop
  • Apache Kafka
  • Google Cloud ML Engine
  • Amazon Machine Learning
  • Azure Machine Learning
  • IBM Watson

Machine Learning Engineer Self Learning Path

Like Data Science, there is a lot to learn to become a Machine Learning Engineer. Assuming we’ve already learned the foundation Math requirements, below I will leave some good resources to help you practice various parts of the ML engineer skillset:

Software Engineering & Programming

Problems – LeetCode

Dashboard | HackerRank

Data Science & Machine Learning

Kaggle: Your Machine Learning and Data Science Community

DataHack : Biggest Data hackathon platform for Data Scientists

Courses

Wrap Up

As Data Science continues to shift from research to production, the demand for ML engineers has been increasing rapidly. If you have a knack for building great software but still love Data Science, the ML engineer path may be the one for you.

Thank You for Reading!

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