How to become a Machine Learning Engineer

Machine Learning Engineering is one of the most hyped-up jobs available today. Since most organizations across the globe are implementing Machine Learning for the betterment and growth of their companies, ML professionals are highly in demand. This blog will give you an overview of some of the most common reasons why a career in this field will give you a boost in your career and secure your future. Machine Learning (ML) is an application or a subset of Artificial Intelligence (AI). It is among the most powerful and highly influential technologies today. This technology helps instill the ability to learn and function in machines and computer systems without the need for any human supervision. Why should you learn Machine Learning? In this digital era of Machine Learning and Artificial Intelligence, at every turn, you will see these technologies surrounding you. For economic and technological growth, the future will be filled with AI devices around the world. Due to this, numerous companies across all industries are adopting these technologies. Thus, they will require ML and AI professionals who can help them implement these and become technologically advanced. To learn more about this technology, register for our machine learning certification in Bangalore, and become proficient in it! As per the Machine Learning Market Research report, this technology is predicted to grow over US$8.81 billion by the year 2022 with a compound annual growth rate or CAGR of about 44.1%. Without even noticing, we pass by several applications of Machine Learning on a daily basis. From this, we can gather that we are so consumed and used to these technologies that they have become an essential and regular part of our day-to-day lives. Over the years, Machine Learning has seen exponential growth. Hence, there is a high demand for these professionals in numerous organizations who have the skills and ability to implement Machine Learning in their systems and use it in the most profitable and effective manner. This technology has become extremely important and essential in enriching our lives which is why organizations are filling their IT positions with professionals who have knowledge and experience in Machine Learning. Before you read about how to become a Machine Learning Engineer, let’s understand who these professionals are and what they do. You must also check out this comprehensive Machine Learning Tutorial to know more! How to become a Machine Learning Engineer? To become a Machine Learning Engineer, you need to have certain skills and also understand the various roles and responsibilities of the professionals in this field. Here are some of the must-have skills and the roles and responsibilities of Machine Learning Engineers that you can refer to. Machine Learning Engineer Skills Some of the necessary skills you need to acquire to become a Machine Learning Engineer are as follows: Experience in programming languages, namely, C++, Python, Java, or R Understanding of probability and statistical concepts Knowledge of applied mathematics, along with algorithms and models Basic understanding of distributed computing Work experience in at least one UNIX tool Knowledge of Hadoop and its numerous applications Roles and Responsibilities of a Machine Learning Engineer Some of the major roles and responsibilities of Machine Learning Engineers include: Making use of the fundamentals of computer science such as algorithms, data structures, complexity, and computer architecture Using numerous mathematical skills to perform calculations and computations, as well as to work with algorithms based on programming Producing project results and also isolating resolution-requiring problems to bring more effectiveness in codes and programs Collaborating with data to develop various data model pipelines Managing the data pipelines and infrastructure to produce the code Creating algorithms on the basis of various statistical models and processes Making use of data evaluation and data modeling strategies to identify patterns and depict future instances Applying relevant Machine Learning libraries and algorithms Analyzing large and complex sets of structured and unstructured data to gain insights and decide techniques Researching and further implementing the latest techniques and practices to improve the organization’s infrastructure