Artificial intelligence (AI) is all about the progression of the intelligence of machines by learning from their environment. Hollywood versions of AI have often explained to us in-depth what could happen if the same AI landed in the wrong hands. For most people, AI is a relatively new concept. However, that is not so. AI has been a subject of study as far back as the 1950s. The only change now is that the scalability of the use of AI in different fields has increased. We now have big data applications that use advanced mathematics as well as software programming.

 What is Machine Learning?

Most of the learning in computers is through explicit programming. However, Machine learning helps you do the same through data. So, you learn from data rather than from explicit programming.  The machine learns through the process of training. The machine learning engineer provides the training data to the machine, which it absorbs using different algorithms.  Precise models are thus produced, based on the computations of that data. Hence the training of the algorithms with the absorbed data is what the output of a machine learning model is based on.

The associations between the elements and the data that exist can be better understood through iterative online learning and training. Some people may argue that the same can be done through human observations as well. But, that is not so due to the complexity and the size of these associations and the patterns of the data. The accuracy of the predictive models is expected to increase using machine learning tools. The same approach may not apply to a different set of data. It is based on the volume and the type of data.

A machine learning engineer is therefore expected to understand such approaches and should also apply the same in different situations. There are four basic approaches namely, reinforcement learning, supervised learning, deep learning and unsupervised learning. The use of data for the learning model required to be created is what the choice of approach is based on. When data is used with labelled data such as identifying monuments using images, supervised learning is used. When the data is unlabelled such as in the case of junk mails, unsupervised learning is used. Reinforcement learning is when the benefits of training data are not put into use, though it is still similar to supervised learning as it uses labelled data. Deep learning involves neural networks that learn data through iterative methods.

The Role of a Machine Learning Engineer

A machine learning engineer is responsible for the evaluation of data streams and come up with the best possible models that polish the information to be returned to fulfil the needs of the organisation. A machine learning engineer helps the systems understand the data, interpret it to conclude to make predictions. 

Becoming a Machine Learning Engineer

A Good Degree

A machine learning engineer needs to deal with applications related to data science, mathematics, computer programming, and computer science. Hence a degree in any of these disciplines is a must for becoming a machine learning engineer. To understand the employer’s data needs, strong business acumen is also required. A management degree can be a good addition to land up in a good job.

Early Career Options

It is important to understand that you may not be able to grab a machine learning engineer job at the start of your career. However, some job profiles can help you move towards this post, including:

• Software Developer
• Software Programmer
• Data Scientist
• Software Engineer
• Computer Engineer

A High-end Degree

A Ph.D. degree in any discipline is always an added advantage for any career prospects. For becoming a machine learning engineer as well, it is good if you can do a Ph.D. in machine learning or at least a Master’s in the said discipline. It is again important to remember that the role of a machine learning engineer is not an entry-level job. A master’s degree can also help you get a faculty-level position in any of the good institutes/universities and even professional institutions such as Apple and Google where they need people to train their employees. Apart from the experience, you gain while working as a software engineer or a developer will always be handy to apply for machine learning engineer jobs.

The performance of the systems also needs to be monitored by a machine learning engineer apart. The evaluation of the data that has been returned through modeling is also looked after by a machine learning engineer. A machine learning engineer may also function as a data scientist in small-scale organizations.

Several programming platforms are used for machine learning such as:

• Java
• C
• Python
• C++
• JavaScript

Several algorithms are used by machine learning engineers for the four approaches like:

• Decision trees
• Support Vector Machines
• Ordinary Least Squares Regression
• Clustering algorithms
• Logistic Regression
• Ensemble methods
• Naïve Bays Classifications

Job Description of Machine Learning Engineer

Although there are specific requirements of the organization when looking for machine learning engineers, some common descriptions include:

• Working with Business Analysts and Data Scientists to frame problems in a business context
• Pulling data from various sources and building pipelines for the same.
• Building and maintenance of learning models and infrastructure for machine learning.
• Running machine learning experiments and tests
• Doing statistical analysis and analyzing the results
• Specific technical knowledge for becoming a machine language engineer include –
• Programming languages such as C, C++, Python, and Java
• Experience of working on machine learning platforms such as Google, Amazon, and Cloud
• Knowledge of statistics, probability, evaluation, and data modeling

The last decade has seen a rapid rise in the job demands of machine learning engineers and is a good job prospect to look out for.