What are the Skills Required for a Machine Learning Engineer?

 What are the Skills Required for a Machine Learning Engineer?

Data Science, Artificial Intelligence, Machine Learning, etc. Are becoming increasingly popular nowadays. 

The problem with these technologies is that they are thrown around like buzz words so many people don't know exactly what they mean or what skills they require to master them. 

We have listed all the different skills you will need to become a Machine Learning Engineer in this article, so read on to learn more!

The goal of machine learning is for machines to learn on their own from data without much human intervention. 

An integral part of this field is Artificial Intelligence in combination with other technologies, such as Data Science.

Who are Machine Learning Engineers?

Engineers specializing in Machine Learning are in high demand. 

By delivering software components that can work with minimal human supervision, they aim to gain insights from data that is provided to them. 

For a Machine Learning Engineer, software development and computer fundamentals are very important because that is their specialization.  

Now let's look at the skills you'll need as a Machine Learning Engineer.

The technical skills required of ML engineers

Expertise in software engineering. 

Machine learning engineers must understand the following computer science fundamentals: designing algorithms to search, sort, and optimize; understanding approximate algorithms; knowledge of data structures, such as stacks, queues, graphs, and multidimensional arrays; computability and complexity; and knowledge of computer architecture, such as memory, clusters, bandwidth, deadlocks, and cache.

Skills in data science. 

Machine learning engineers rely on the following data science fundamentals: a working knowledge of programming languages such as Python, SQL, and Java, hypothesis testing, data modeling, as well as knowledge of mathematics, statistics, and probability (such as Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, and Bayes nets, HMM, etc.), as well as proficiency with evaluating predictive models and algorithms.

The soft skills that ML engineers need

Effective engineers possess soft skills that set them apart from those who fail. 

A machine learning engineer is, at its core, a technical professional, but soft skills such as being able to clearly communicate, solve problems, manage time, and collaborate with others are what make a project successful.

Communication skills are important. Machine learning engineers often work with data scientists, analysts, software engineers, researchers, marketing teams, etc., and product communication is key to successful project teams, so clearly conveying the project goals, timelines, and expectations to stakeholders is essential.

Management of time. Often, machine learning engineers have to meet the demands of different stakeholders while still managing to perform research, plan and organize projects, design software, and test it rigorously. 

ML engineers need to master these tools/programs

Many machine learning engineers are proficient in the following programs and tools in addition to Python, SQL, Java, and C++ :

Flow processor Tensor

Hadoop and Spark

Programming in R

Apache Kafka

Weka

MATLAB

Google Cloud Machine Learning Engine

Amazon Machine Learning

Summary

We have discussed in this post the skills that one requires to begin their journey in machine learning. The future of machine learning is certainly bright.

If you are curious about Machine learning, you can learn everything you need to know at Near Learns Machine learning training.