Role of Machine Learning Engineer in Deploying advanced Cyber Security Systems 


Computers get smarter daily using advanced technologies like artificial intelligence (AI) and machine learning (ML). As the name implies, the techniques of artificial intelligence help to build brains within computers to enable them to perform various tasks by emulating human behavior. Artificial intelligence helps computers learn from the environment, stimulating their intelligence over time. The technology of Machine Learning aids the process of learning; hence artificial intelligence and machine learning go hand-in-hand. The rapid and widespread applications of machine learning have opened new career opportunities for computer engineers, evident from the emergence of a new breed of computer engineers who are specialists in machine learning. Today, the career of a machine learning engineer is highly lucrative because it fetches not only high-paying jobs but also ensures speedy growth to help your career reach new heights. 

Tremendous growth prospects for a machine learning engineer

According to the United States Bureau of Labor Statistics, between 2016 and 2026, there will be an increase of 13% in computer-related occupations, with machine learning comprising a major share of the growth. Indeed, the demand for machine learning engineer jobs will rise steeply, as evident from the vast adoption of machine learning across all industries. 

Enterprises across sectors are attaching immense importance to machine learning by applying the technology across their organizations resulting in a huge spike in global AI funding. The overall growth of the machine learning market is likely to shoot up from $1 billion in 2016 to $8.81 billion by the end of 2022, an incredible eight-fold rise. Moreover, considering that the allocation in machine learning research during the first quarter of 2019 was a whopping $28.5 billion, it’s easy to imagine the kind of career-boosting that can happen in the field in the coming years. 

Although opinions vary about the scale and scope of the future adoption of artificial intelligence and machine learning, there is consensus among industry experts that machine learning will continue to impact society and shape businesses. 

How machine learning works

Aspiring machine learning engineers should have clarity about the emerging field of computer engineering and its applications for understanding the subject better. It should help make well-informed decisions about taking up machine learning as a career. The knowledge should help you know how to become a machine learning engineer and whether you are ready for it. 

While AI helps computers become smart, Machine Learning works in the background and teaches machines how to become smart. AI cannot flourish without help from Machine Learning, and it will not be wrong to say that Machine Learning holds the center stage in all kinds of technological advancements. 

Machine learning stimulates artificial intelligence, considered the brain of computers, enabling machines to think independently without any external assistance. Computers can then act by themselves without the need for programming. Instead of humans programming computers to accomplish specific tasks, as with traditional computers, machine learning allows humans to provide data and describe what they want the program to do. 

Computers use that data to train themselves first and then work out ways to carry out the tasks with the help of algorithms. During operation, a computer gathers more data over time which helps it get smarter. However, data labeling is a key factor in enabling computers to correctly use the data. For example, if you want a program to sift through photos of pepperoni pizza and ice cream, you must first label some photos to help the algorithms understand what ice cream and pepperoni pizza look like. 

Deep learning and its working modality

Understanding data labeling is also important in distinguishing between machine learning and deep learning. Deep learning is a subset of machine learning but follows a different learning method. Instead of relying on data labeling, deep learning relies on neural networks that draw inspiration from the human brain both in name and structure. 

To allow computers to distinguish between pepperoni pizza and ice cream by sorting the photos with the help of deep learning, you must provide a significantly larger set of photos. The computer then deploys the neural networking to sift the photos through several layers of processing to distinguish the pepperoni pizza from the ice cream, one step at a time. The primary layers look at the basic properties like edges or lines between dark and light parts of the images, while the subsequent layers help to identify more complex features like faces and shapes. 

Helping computers see and even understand natural language

The gamut of Machine Learning is useful for performing a wide range of tasks and applications besides solving problems. Computers attain the power to see, a phenomenon known as computer vision that helps machines make sense of videos and images. Moreover, Natural Language Processing (NLP) is a fast-growing subset of machine learning that aid computers in making out the meaning of unstructured text. Thus, the role of the machine learning engineer Google is evident when Google processes voice searches and deciphers lengthy text searches. Over and above, voice and speech recognition powers smart assistants like Google Assistant, Amazon’s Alexa, and Apple’s Siri. The smart assistants that helped households get a taste of smart living introduced AI to many consumers for the first time. 

Across industries, machine learning applications are gaining momentum through applications in products and within organizations to streamline business processes. For example, machine learning can streamline and augment supply chain operations and is extensively used in business analytics, marketing and sales, and cyber security. 

Machine learning helps in bolstering cybersecurity.

Machine learning contributes significantly to deploying cyber security technology more effectively, as a Facebook machine learning engineer admitted. Cyber threats areincreasing fast andgetting more complex and sophisticated while posing tremendous challenges for IT teams to protect their networks and systems. As organizations increasingly move their applications and systems to the cloud, the range of security threats is increasing daily. To create a strong defensive ring against advanced cyber threats, machine learning can help analyze patterns and learn from them to prevent similar attacks while responding to changing behavior.

 Armed with Machine Learning, the sentinels of cyber security become more proactive and can pre-empt threats besides preventing cyber-attacks successfully. They are always ready to respond to active attacks as and when they occur. Machine learning can significantly reduce the time spent on routine tasks, allowing organizations to use their resources for more productive purposes.

Data is crucial for the successful deployment of machine learning in cybersecurity.

Machine Learning processes are entirely data-driven for developing and using those patterns with algorithms. Gathering extensive and rich data from every area relevant to the business domain is essential for developing patterns. The data should help to figure out the maximum number of potential outcomes from an identical number of scenarios as possible. The quality of data is as much important as the quantity. Data must be relevant, complete, and rich in context, collected from every potential source, whether within the network, at the endpoint, or in the cloud. To define outcomes, you must depend on clean data that makes sense by focusing on data cleaning. 

It begins with the right approach to data.

The right approach to data is critical in deploying cyber security systems powered by machine learning. To ensure that organizations leverage machine learning in their cyber security strategies, business leaders and the top management teams focus on collecting data and its organizing and structuring. Data quality is critical because it must contain information that helps understand everything that happened, not just the security threats. 

The data has to be rich to contain every detail about applications, machines, network sensors, and protocols. Data should help to co-relate what happened in the interim between whatever is visible on the network and what you can see at the endpoint. This is the view of a machine learning engineer at Google who oversees cyber security. He explains that stitching the data together is part of the work so that the full picture gives a single representation. Then only you can build different models, create models to capture different aspects of behavior, and then with the help of algorithms, make decisions about when to trigger alerts, when to act in response to potential threats and when to build protections to pre-empt threats. 

What you need to become a machine learning engineer

A glance at some machine learning engineer interview questions should help to gather insights about the eligibility of machine learning engineers and their roles. The aspiring candidate should have a bachelor’s degree in science, computer science, or a related field. Advanced educational qualifications like a master’s or a doctorate will surely give an edge in the competition. Higher qualifications make you eligible for managerial roles with much higher salaries, while a bachelor’s degree helps to make a start at the entry-level. 

Besides the educational qualification, acquiring the skills of machine learning through hands-on learning or on the job is essential to draw the attention of employers. To excel as an ML engineer, you must demonstrate your specific skills to impress employers and enhance your chances of selection.   

Here are some must-have skills for ML engineers that employers usually look for. 

 Distributed computing – This branch of computer science deals with a network of computers instead of a single computer. Since machine learning engineers have to handle large volumes of data that keep growing throughout their careers, knowledge and experience in handling distributed computing networks should come in handy. 

Natural language processing (NLP) – Since the task of an ML engineer is to facilitate the learning process of computers by helping the machines interpret human speech, knowledge, and experience, NLP is essential. As an engineer, you must have the necessary skills to deal with NLP to ensure that computers respond appropriately to human speech. 

Applied mathematics – Mathematical skills are a basic requirement for computer engineers, and it is also good for ML engineers. After all, all ML engineers are computer engineers but not all computer engineers are ML engineers. On analyzing the machine learning engineer interview questions, it becomes clear that employers would test your mathematical abilities like linear algebra, probability, and algorithms and how you apply these for developing machine learning models. Advanced mathematical and data analytical skills are critical for advancing in your career.

Written and communication skills – ML engineers must be effective communicators and have a strong command of written and verbal communication because they must often present their processes and results to the top management and stakeholders outside the organization.  

Familiarity with algorithms is a must.

Machine learning engineers must be familiar with various machine learning platforms that provide the foundation for complex programs for ingesting data. Data analytics helps make the most accurate predictions, identifications, or output the model has to deliver. The programming languages mostly used include Python, Java, C, C++, JavaScript, R, Scala, and Julia. 

Moreover, familiarity with the standard algorithms is essential for modeling and programming, which are routine tasks of machine learning engineers. Although customized algorithms are sometimes necessary, and even tweaking some standard algorithms, ML engineers must have sound knowledge about algorithms built upon the pillars of supervised and unsupervised learning, deep learning, and reinforcement learning. 

Some of the most widely used algorithms are Decision Trees, Naïve Bays Classification, Ordinary Least Squares Regression, Logistics Regression, Support Vector machines, and Ensemble Methods. Clustering Algorithms, Principal Component Analysis, Singular Value Decomposition, and Independent Component Analysis are some other algorithms that machine learning engineers must be thoroughly conversant with. 

The role of Machine Learning Engineers

To clarify the role of an ML engineer, you can look at any model machine learning engineer’s resume. The first thing that should catch your eye is that the candidate has advanced programming, data science, and mathematics skills that are useful for evaluating data streams, which is the primary task of ML engineers. Based on data analytics, they work out the best ways to develop data models that provide precise and relevant information which can be useful for realizing the organizational goals. 

After writing the programs, ML engineers provide the necessary data while helping the system to learn the techniques of interpreting data for drawing conclusions or making predictions. After the system undergoes sufficient training, it goes live in any desired setting. In the next phase, ML engineers are responsible for monitoring the system’s performance and evaluating the data gathered through the data models while ensuring its accuracy.  

However, the role of ML engineers varies according to the organization’s size. In larger companies, ML engineers collaborate with data scientists to achieve machine learning objectives. However, in smaller companies, ML engineers have to double as data scientists while discharging their responsibility to create an optimal machine learning system and provide clean data. Data scientists will then work on the data to deliver the required data. 

Technical Knowledge and Experience necessary for ML Engineers

Employers are keen to hire ML engineers with specific experience in the following areas.

  • Experience in machine learning platforms such as Google Cloud, Microsoft Azure, Amazon Web Services, and IBM Watson.  
  • Knowledge of programming languages necessary for machine learning, such as Python, Java, JavaScript, C, C++, Scala, and R.
  • Sound knowledge and expertise in statistics and probability.
  • Experience in using machine learning algorithms and libraries. 
  • Experience in Data modeling, including evaluation of data models 
  • High skills in software development. 

Job Outlook and salary for ML engineers 

The future looks quite bright in the field of Machine Learning within the landscape of data science. According to Forbes’ forecast, the demand for professionals with AI and ML skills is growing at an astonishing rate of 71% through 2025. The report also confirms that currently, 200,000 vacancies are available for AI and ML professionals in high demand across industries ranging from manufacturing to information technology and from marketing and advertising for businesses to fintech and insurance.  

According to Payscale, the average annual salary of machine learning engineers is $112,806. In addition, they receive a share of the profit and bonuses. At the entry-level, the average annual salary of a machine learning engineer is $93,000, which goes up to $138,000 for those with experience ranging between 5 and 9 years. The earnings go up exponentially with higher experience, and ML engineers with 20-year experience earn $162,000 per year.

As businesses are rapidly integrating technology to streamline their business processes and reinforce the security of their IT systems, the demand for machine learning engineers is gathering pace. Only machine learning engineers can help businesses operate safely and securely amid rising cyber threats with higher efficiency and improved productivity. As more funds flow into machine learning research, opportunities are multiplying fast, and it is the right time for computer engineers to make a career shift toward machine learning. 

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