Data science is an interdisciplinary field that has so many related areas, such as machine learning or big data. As the industry moves forward, the similarities and distinction between data science-related fields are also defining more concretely. Machine learning engineering is a very close field to data science, and in some companies, there is almost no distinction between these two career paths. What is precisely machine learning engineering?
Machine learning engineering is a sophisticated area of software engineering that focuses on developing smart software that can automate human-like tasks with the power of artificial intelligence and machine learning technologies. Comparing to data scientists, machine learning engineers work more on the software engineering side rather than the analytics side. As a machine learning engineer, you will be doing mostly development work towards integrating artificial intelligence into software solutions. Among the most prominent areas that machine learning engineers nowadays work on are autonomous driving and cloud computing.
In the 2010s, the attention of machine learning researchers went back to deep learning algorithms. Besides, training deep learning algorithms become more feasible because cloud computing technologies introduced higher computation and storage power available to individuals and businesses with a rather low price. Also, and with the emergence of social media in the era of big data a lot of training datasets became available for the experts to boost their machine learning models.
The demand for machine learning engineers has been steadily increasing. Nearly all companies, no matter corporate or startup, are looking to adopt artificial intelligence technology for their businesses. During the period between 2015 and 2018 alone, the number of machine learning engineering job postings grew by 344%. This growth rate puts machine learning engineers or artificial intelligence specialist roles as one of the hottest job titles in 2020.
There are several skills required to become a highly-rated machine learning engineer. Firstly, you need to computer programming, like Python or R, and software engineering skills are a total must. Sound knowledge of probability & statistics, as well as machine learning algorithms, is necessary. One needs to know how to train a machine learning model and how to use the trained model for prediction tasks.Machine learning algorithms are applied to a wide range of problems, and knowing which model is the most effective one for solving each problem is the key to this job.
Practical experience with machine learning frameworks are indeed essential. Nowadays, almost every machine learning algorithm is already implemented in a library or framework. There is a large community of academic and industry experts providing existing resources for both traditional machine learning and deep learning algorithms. Existing technologies, like scikit-learn, Tensorflow, Keras, and PyTorch, and cloud solutions from large tech companies like Amazon, Microsoft, and Google, make the work of machine learning engineers much simpler. However, knowing the best practices in the adoption of these technologies to the specific use case is very important and cannot be overlooked.
Besides, training the models usually follows a specific pipeline for data acquisition, data preparation, exploratory data analysis, and evaluation of model performance. For this, a machine learning engineer needs to do tasks that are related to data engineering and data science as well. The trained models often need to be deployed in operational software and existing architecture with high levels of complexity. Therefore practical knowledge of software engineering is also required.
Making the transition from data science to machine learning engineering is rather straightforward. One needs to show the engineering mindset and ability to deliver practical results in a fast and reliable way. Such a candidate also needs to have some knowledge from the domain. After all, delivering useful results that meet a business demand is the key to success in every job in the data science field.
Finally, some emerging fields have grown so prominent in recent years that they are becoming independent from data science and machine learning engineering fields. These fields are like computer vision engineering, speech technology engineering, and natural language processing engineering.