Original article was published by Pouyan R. Fard on Artificial Intelligence on Medium
Working as a machine learning engineer
Career strategies for AI & data talents
Data science is an interdisciplinary field with 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 software engineering area that focuses on developing smart software that can automate human-like tasks with the power of artificial intelligence and machine learning technologies. Compared to data scientists, machine learning engineers work more on the software engineering side than the analytics side. A machine learning engineer will be mostly developing 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 has become more feasible because cloud computing technologies introduced higher computation and storage power available to individuals and businesses at a relatively low price. Also, and with the emergence of social media in the era of big data, many 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. Between 2015 and 2018 alone, there was growth of 344% in the number of machine learning engineering job postings. This growth rate puts machine learning engineers or artificial intelligence specialist roles as one of the hottest job titles in 2020.
There are several requirements for becoming highly-rated machine learning engineer. Firstly, you need computer programming skills, like Python or R, and software engineering skills are an absolute 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 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 is indeed essential. Nowadays, almost every machine learning algorithm is already implemented in a library or framework. A large community of academic and industry experts provides 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 adopting 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 model performance evaluation. For this, a machine learning engineer needs to do tasks related to data engineering and data science. The trained models often need to be deployed in operational software and existing architecture with high complexity levels. Therefore practical knowledge of software engineering is also required.
Making the transition from data science to machine learning engineering is relatively straightforward. One needs to show the engineering mindset and ability to deliver practical results quickly and reliably. Such a candidate also needs to have some knowledge from the domain. After all, delivering useful products 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 that they become independent of data science and machine learning engineering fields. These fields are like computer vision engineering, speech technology engineering, and natural language processing engineering.
About the Author:
Pouyan R. Fard is the Founder & CEO at Fard Consulting & Data Science Circle. Fard Consulting is a Frankfurt-based boutique consulting company serving companies in various industries. Pouyan has years of experience advising companies, from startups to global corporations, on data science, artificial intelligence, and marketing analytics. He has collaborated with Fortune 500 companies in pharma, automotive, aviation, transportation, finance, insurance, human resources, and sales & marketing industries.
Pouyan is also leading the Data Science Circle team to build a career hub between employers and data science talents. DSC’s mission is to nurture the next generation of data scientists through career training and helping the employers to find top talents in big data.
Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.