Machine Learning Engineers Are In High Demand. So, What Do They Do?

With every organization digitizing its operations and taking advantage of data science tools, artificial intelligence, machine learning, the demand for professionals in their domain is always high. With machine learning being an important aspect of all automation tools, machine learning engineers are in the highest demand.

According to Brandon Purell, Senior Analyst at Forrester Research, "one hundred percent of any company's future success depends on adopting machine learning. For companies to be successful in the age of the customer, they need to anticipate what customers want, and machine learning is absolutely essential for that."

Let's understand why the demand for a machine learning engineer is more than ever.

Role Of Machine Learning

Machine learning engineers are a combination of two vital roles in the industry, data scientist and software engineer. While the main focus of a data scientist is to work with big data, a software engineer does the coding of a program. The job of a data scientist is analytical where they use a combination of mathematical, statistical, analytical skills, and machine learning tools to process and analyze massive pools of data for business insights. Whereas, software engineers are experts in writing scalable codes for programs and design complex software systems for companies. Their roles don't require working with machine learning tools.

The applications created by data scientists are difficult for software engineers to understand as they are complex and have no design pattern. This is why companies are looking to hire machine learning engineers who can put both the skills to work. A good ML engineer in this day and age should be to understand the data scientist's code and make it more accessible.

Responsibilities Of A Machine Learning Engineer

A machine learning engineer's work is similar to a data scientist's role, both work with huge datasets. Hence, a ML Engineer should have excellent data management skills. Their job roles require them to combine the rules of data science with programming to help companies leverage their business with AI and machine learning technologies.

Necessary Skills For A Machine Learning Engineer

• Soft skills – These non-technical skills help an engineer keep up with the dynamic nature of machine learning. An engineer must know efficient time management and possess business knowledge for fast ideations.
• Technical Skills – Basic technical skills like intermediate-level Python, C++, and basic mathematics concepts like linear algebra, calculus, and statistics is a requirement that companies look for while hiring.
• Machine learning & neural networks – Machine learning and neural networks are important skills to find accurate solutions for business problems. As machine learning extends beyond neural networks, knowledge of non-neural network concepts like algorithms is a bonus.

What Does A Machine Learning Engineer Do?

Machine learning engineers work closely with data scientists. While data scientists extract meaningful insights from several GBs of datasets and communicate the insights to stakeholders. Machine learning scientists make sure that the models used by data scientists can analyze large amounts of data in real-time for getting accurate results. When these disciplines work together, they create technologies for companies that were once considered impractical and impossible. ML Engineers are paving the future of the tech world by enabling several industries to leverage disruptive technologies.