Top 5 Skills You Need to Become a Machine Learning Engineer
Due to the consistent advent in science and technology, the area of machine learning has now become a growing field. It is also getting a lot of attention. However, getting jobs in machine learning has become relatively difficult. Landing a job as a machine learning engineer at a big organization could mean having a comprehensive skillset.
This could include having various skills such as system design and programming. Moreover, there is a lot of research and learning involved for the purpose of preparing and also applying for a new position. You should also understand the fact that being a machine learning engineer requires having a comprehensive understanding of the overall ecosystem that one is designed for.
Over the years, more and more professionals have illustrated their personal interest in Machine Learning on a regular basis. In fact, there are very few fields that have generated more hype these days than this one. This post would help explain to you to have an understanding of the top 5 skills you need to become a machine learning engineer. You will be able to understand how to develop an understanding as well as the overall mindset along with the specific skills you need to start working as a machine learning engineer.
Programming skills and computer science fundamentals
One of the most important aspects of becoming a machine learning engineer is learning the core programming skills and computer science fundamentals. Some of the core topics include algorithms, data structures, computability and complexity and computer architecture. One should be able to implement, apply, adapt and address them when programming. Coding competitions, practice problems, coding competitions and hackathons could prove to be some of the best ways to
improve these skills.
Statistics and probability
Having a characterization of probability along with the different techniques derived from it tend to be at the absolute core of several machine learning algorithms. They are also an important aspect of dealing with uncertainty in the real world. Closely related to this is the area of statistics. It usually provides different important measures, distributions and analysis methods pivotal for the purpose of validating and building different models through the means of observed data. Several machine learning algorithms are also extensions of statistical modeling procedures.
Evaluation and data modeling
From a technical perspective,
data modeling could be illustrated as the process of estimating and evaluating the structure of a particular dataset. The emphasis is to find useful patterns along with predicting specific properties of instances that were previously unseen. An essential component of this process of estimation is consistently evaluating how beneficial a specific model is. Depending on the available task, one is required to select the accuracy/error measure as well as the evaluation strategy.
Application of libraries and machine learning algorithms
Having a standard execution for the purpose of a machine learning algorithm is available extensively through the means of libraries/packages/APIs. However, effectively applying them requires having a highly suitable model, a learning procedure that can also be effective for the data and also understanding how hyper-parameters could have an influence on learning.
It is also essential to have awareness of the various benefits and drawbacks of various approaches that could well trip you in the near future. Machine learning and data science challenges could prove to be a highly effective way for getting exposed to various kinds of problems as well as their nuances in the long run.
System design and software engineering
It is essential to note that a machine learning engineer’s usual deliverable or output is software development. Usually, it is a small component that can directly fit into a large ecosystem of different services and products. It is essential to understand how some of these different pieces could work together. As a result, it is essential to create appropriate interfaces for a specific component that could be helpful for you in the long run.
Having a prudent and careful system design is essential for avoiding any potential bottleneck and allowing your algorithms to scale well through the consistent increase in data volume. Best practices of software engineering could prove to be highly invaluable for the purpose of collaboration, productivity, quality and maintainability in the long run.
