Today, machine learning is ubiquitous. Many data-centric organizations are rapidly adopting machine learning to explore opportunities for data monetization. Business leaders recognize the immense amount of insights they can harness from tapping into enormous amounts of data at their disposal. Sadly, they lack the time and expertise to sort through big data.
Machine learning engineers work as part of a larger data science team and their primary objective is to create machine learning models and training systems. Continue reading to learn about the roles and responsibilities of a machine learning engineer.
What is machine learning?
In this digital age, various sectors of the global economy have to deal with substantial amounts of data in different formats from multiple sources. Advancements in technology, cloud storage, and computing capabilities have led to a rapid increase in the availability and consumption of big data. Various industries are increasingly adopting machine learning for big data processing as machine learning is a critical aspect of artificial intelligence (AI). This allows a computer or machine to learn and adapt to changes in data without human intervention.
Machine learning finds application in different sectors. For example, lending institutions can adopt machine learning to accurately predict bad loans and develop a credit risk model. The possibilities inherent in machine learning in the digital era are endless. A machine learning engineer can help organizations maximize the opportunities that massive amounts of data present.
What does a machine learning engineer do?
Machine learning has become an invaluable resource for modern businesses. For companies to be successful and remain profitable, they need to stay ahead of changing consumer behavior, and machine learning can help with that. With a growing number of companies exploring and leveraging the powerful tool of machine learning, ML engineer roles have undoubtedly become the most in-demand data science roles.
It’s true that many business owners aren’t tech-savvy enough to build ML algorithms into computer or machine programs. That’s where a machine learning engineer comes in—to connect data science to an organization’s bottom line. The role of machine learning engineers cuts across that of data scientists and software engineers. While data scientist roles are more analytical, the core focus of a software engineer is on programming. Machine learning engineer jobs focus on working with massive amounts of data to design self-running predictive models. Generally, ML engineers work closely with data scientists.
While data scientists work to derive actionable insights from large datasets to inform a business decision, ML engineers ensure the models utilized by data scientists can efficiently process big data. Additionally, machine learning engineers perform statistical analysis and optimize ML models using test results. They continually train computer systems to learn and adapt to new data and perform data visualization and distribution.
Skills required to be a machine learning engineer.
As mentioned, the role of machine learning engineers is somewhat similar to that of a data scientist role. An ML engineer needs software engineering and data science skills to succeed in the ML engineer role. They also need to learn to write computer codes such as Python, which is a language used to build ML applications.
Hopefully, this article enlightened you more on the field of machine learning. To increase your chances of getting high-quality jobs in machine learning, consider acquiring a bachelor’s degree in computer science, statistics, math, or any related discipline. Additionally, think about getting a master’s degree in artificial neural networks, machine learning, deep learning, or related fields.