In the field of information technology (IT) today, the role of a machine learning (ML) engineer is currently one of the most popular. Of the US managers surveyed in a recent Robert Half report,
- About 30% said their company is currently using artificial intelligence (AI) and ML
- About 53% said their company plans to adopt these technologies over the coming three to five years
It is clear that as more organizations look towards AI and ML in their day-to-day operations, their need to hire ML specialists will only go up!
So why exactly are
machine learning engineers in demand? From Internet-of-Things (IoT) networks to interactions with customers, data comes in from numerous sources – and in huge quantities – to the typical organization of today. This implies that it is next to impossible to process all this data manually, and thus ML is indispensable for taking full advantage of the available data to uncover valuable insights.
There are a number of applications for ML, the top ones of which are explained below,
- Customer insights
Something commonly used by e-commerce platforms, association rule learning – which refers to how ML software makes connections – is at the heart of the algorithms that drive e-commerce and offer suggestions to buyers of one product about what other product(s) they might want to consider.
- Image and speech recognition
From converting text to speech (and vice versa), to auto-tagging images with the names of the persons or places in the image(s), algorithms created by ML specialists do excellently at tasks that require converting a mass of unstructured data into some useful information.
- Fraud prevention and risk management
By sifting through huge volumes of historical data, ML algorithms can make financial predictions that range from how investments will perform in the future to the risk of loan default. Even fraudulent transactions can be identified in real time, by leveraging regression testing.
But what exactly does a ML professional do? In the real world, the job of a data scientist and a ML engineer is pretty close, as both:
- Use huge quantities of information
- Require excellent skills in managing data
- Need the ability to perform complex modeling on dynamic data sets
There are, however, some major differences. Most importantly, the output of the work by a data scientist is insights presented to a human audience, typically as charts or reports. On the other hand, a ML engineer designs software that runs by itself for automating predictive models. Every operation performed by the software informs future operations so that those are carried out with higher accuracy – hence the term “learning”.
Closely related to AI, ML includes deep learning (DL), a subfield that uses artificial neural networks that work on multilayered (deep) data sets to “think” and solve complex problems. Chatbots, driverless vehicles, and virtual assistants are some of the commonly-used examples of DL, and these will improve in terms of accuracy and practicality, with time.
Recommendation engines, deployed by many consumer-facing services, are a common example of ML. A user searching for a product on Amazon or watching an episode on Netflix means data generated, which is fed to an algorithm whose recommendations to users become more accurate as more data comes in. And all this is with no human intervention!
What responsibilities does a ML specialist handle? The key task is to refine raw data by putting it through big data tools and programming frameworks. This then goes into data science models, ready to be scaled as and when needed.
Combining software engineering and data science, ML professionals must feed data into models created and defined by data scientists. The role is in fact far more specific than that of a data scientist and is reflective of a clear, focused approach to how the organization wishes to use ML in its operations. On the other hand, a data scientist could just possess programming and ML knowledge in parts, while a ML engineer would have specialized in ML as a part of software engineering.
Another task of a ML professional is taking a data science model from the theory stage to actual production, working in real time on massive data amounts. They seek to help machines analyze information, identify patterns, enable deep insights, and make decisions based on the findings from algorithms they create.
Clearly, all of this requires a ML engineer to possess a diverse set of skills. The top requirements on the programming and technical fronts are as below,
- Programming skills needed
- Computer science fundamentals and programming
- Distributed computing
- Machine learning algorithms and libraries
- Software engineering and system design
- Unix
Technical skills needed
- Advanced Signal Processing Techniques
- Data Modeling and Evaluation
- Natural Language Processing
- Neural Network Architectures
- Reinforcement Learning
What salaries can a ML specialist expect to earn? This varies as per where on the career ladder the person has reached. The following helps to understand expected salary levels better:
- Entry-level
For someone with up to four years of experience, the salary ranges from USD 66,000 to USD 130,000, with an average of USD 97,579. At the higher end of the range, salaries get boosted by the inclusion of bonuses and profit-sharing arrangements.
- Mid-level
A ML professional with five to nine years of experience could earn an average salary of USD 112,095. The range could be from USD 75,000 at the lower end to more than USD 150,000 at the upper end, and possibly even higher with bonuses and profit-sharing.
- Senior-level
With more than a decade of experience, professionals at this level take home the highest pay packages. The average is USD 132,500, and the range is from USD 102,000 to more than USD 180,000 due to fierce competition, bonuses, and profit-sharing.
Choosing the right roadmap is critical to a successful career in ML. Important steps to take are explained below,
- Education
The level depends on the position aspired for. The bar is lower for an ML engineer, who could do with a college degree and look to work on relevant projects. For a ML scientist, companies often require a Masters or a Ph.D.
- Internships
Given the short supply of skilled professionals, it is a great idea to intern while in college, as this gives practical knowledge and real-life experience, along with attracting the attention of potential employers. An internship could also lead to a full-time job offer.
- Skills
It is important to stay up to pace on programming with C++ and Python, as well as big data and analytics, NLP, image processing, and computer vision.
- Certifications
For an ML professional, getting an AI/ML certification is an excellent decision. There is a growing demand for skilled professionals, and it helps to make oneself a more attractive job candidate for potential employers. Certified AI/ML professionals are well-placed to take on new and unconventional career paths created by AI, and also earn higher salaries than people without certifications.