Engineering Machine Learning: A Tutorial

Contrary to popular science fiction culture, machines are not all doom and gloom. Thanks to the efforts of Machine Learning Engineers, Artificial Intelligence (AI) has evolved significantly. Now, it is recognised for more favourable attributes such as spam filtering, voice recognition support for virtual assistants, and recommendation systems, rather than anything resembling Skynet.

Machine Learning Engineering has a goal of creating intelligent machines capable of analysing and learning from their own data. This data is then used to build and train a neural network tailored for data analysis. It can be easy to conflate Machine Learning with concepts like Artificial Intelligence, Deep Learning, and Pattern Recognition. However, Artificial Intelligence is a branch of Machine Learning, while Deep Learning encompasses it. While Machine Learning and Data Science share similarities, there are significant differences between them. Machine Learning has roots in both Pattern Recognition and Computational Theory.

The purpose of this article is to offer a peek into the world of Machine Learning Engineering, covering the fundamental aspects of Machine Learning, the distinctions between Deep Learning, Artificial Intelligence, and Machine Learning, the roles that Machine Learning Engineers and Data Scientists serve, the qualifications required for a career in Machine Learning Engineering, the practical applications of Machine Learning, and the employment prospects available for those who choose this specialism.

Machine Learning – A Mechanism

At its core, this example showcases the basic workings of machine learning.

If you are aspiring to construct an independent model aircraft that can accomplish complex manoeuvres such as take-off, landing, etc., it would be unwise to avoid Machine Learning. Attempting to develop an algorithm or writing software that would respond to sensory inputs for adjusting the control surfaces of such an aircraft would be a daunting task due to the considerable knowledge required of the aircraft’s particular specifications and the data needed for stunts.

While working with models, it is equally important to consider the various factors that must be adapted. Machine Learning enables a model to receive data, and based on the results of test flights, it can automatically adjust itself to compensate for any discrepancies. By handling all the intricacies of fine-tuning intricate details, Machine Learning experts and engineers just need to design the initial learning programme. This approach makes the procedure dramatically more flexible than it would be without the intervention of Machine Learning.

While deep learning is often linked to neural networks, Machine Learning is not. The latest updates to the diagram acknowledge the current advancements; the relationship between Deep Learning and Machine Learning remains unchanged, but now both of these intersect with Artificial Intelligence instead of being part of one another.

Machine Learning is critical to both computer vision and spam filters. While the two fields share some commonalities, there are also notable distinctions that must be acknowledged.

It is critical to highlight the variations between Machine Learning (ML) and Artificial Intelligence (AI); there is some degree of overlap in the two fields, but they each have distinct methodologies and objectives. To simplify, we have listed the differences below.

Artificial Intelligence (AI) Machine Learning (ML) Deep Learning (DL)
Artificial Intelligence focuses on enabling machines to mimic human behaviour. Study that uses statistical methods to learn through data to make decisions based on precedent. Deep Learning uses neural networks for tasks such as classifications of data.
Artificial Intelligence has been often represented as both a superset of Machine Intelligence and as overlapping with Artificial Intelligence and Deep Learning. Machine Learning is a superset of Deep Learning. Deep Learning neural networks for Machine Learning.
Artificial Intelligence focuses on computer algorithms along with the components of Machine and Deep Learning that are useful for its goal. Machine Learning focuses on the statistical methods for studying data to make decisions based on precedent. Deep Learning focuses on neural networks for tasks such as classification after being adjusted on a training set.
The categories include Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. The categories include Supervised, Unsupervised and Reinforcement Learning. The categories include unsupervised pre-trained networks, convolutional neural networks, recurrent neural networks and recursive neural networks.

A Comprehensive Comparison between Data Science and Machine Learning

Machine Learning (ML) Data Science (DS)
Machine Learning enables machines to pick up on patterns by using data to learn without being explicitly programmed to do so. Data Science is the field of study related to extracting information from structured and semi-unstructured data.
It uses data science as one of its components but is narrower in study. Data Science overlaps as a component of Machine Learning while still being a super set to it.
The categories of machine learning are: unsupervised learning, reinforcement learning and supervised learning. Data Science on the other hand can be divided along its processes i.e. data gathering, cleaning, manipulation, etc.

On comparing the practical applications, we can observe the differences between Machine Learning and Neural Networks. For instance, a Neural Network that can recognise shapes on a 20×20 grid necessitates capturing an image with a broad range of pixel intensities, followed by multiplying each intensity value with its corresponding weight and then summing them. The decision neuron generates a 0 or 1 based on whether the total value surpasses a particular threshold. Although this example pertains to a mere 400 pixels, a single layer, and two outputs, today’s Neural Networks can contain up to 100 layers, 1000 possible outputs, and accuracy levels higher than those attained by humans. This exemplifies the complexity of these self-learning machines and the broader span of Machine Learning.

Essential Technical Competencies for Machine Learning Engineers

The skills, knowledge, and/or qualifications of a Machine Learning Engineer can be divided into the following categories:

Qualifications in the Following Areas

  • Completion of a four-year undergraduate programme in Computer Science, Statistics, Mathematics, or a related discipline.
  • Possession of a graduate degree in a field related to Artificial Intelligence, such as Deep Learning, Machine Learning or Neural Networks.
  • An educational background in Information Technology, Artificial Intelligence, or a related discipline that has extensive commonalities with Machine Learning is highly advantageous. Furthermore, possessing a strong inclination to learn new languages and cultures would be beneficial.

Proficiency in the following domains through practical work experience

  • Programming languages such as Python, R, Java, and others, which are used for software for Machine Learning.
  • These languages are chosen because they offer libraries and frameworks that are highly suitable for the intended purpose.
  • Machine Learning libraries include Matplotlib, Scikit-learn, TensorFlow, and Keras frameworks and packages, among others.
  • Techniques for managing large amounts of data, such as Hadoop, Spark, Pig, and others.

Understanding theoretical content in the following areas

  • Statistics and Data Management
  • Predictive Analysis
  • Corporate framework regarding Computing and Software

Contributions and Responsibilities of Machine Learning Engineers

Machine Learning Engineers hold an exceptional skill to connect software engineering with data science. They frequently form part of data science teams, working with data scientists, engineers, administrators, analysts, data architects and other professionals. Their tasks include creating AI algorithms for prediction and analyzing resulting data. In short, their responsibilities consist of:

Data Collaboration:

  • To locate pertinent datasets for training a Machine Learning model.
  • Evaluating the data quality of the model.
  • We transform and modify scientific prototypes.

Machine Learning-related Tasks

  • By implementing AI or ML algorithms in production.
  • Refining Machine Learning models via statistical analysis and experimentation.

Compensation for Machine Learning Engineers

Income benchmarks for Machine Learning Engineers can fluctuate substantially due to factors such as the regional job market, the cost of living in the location, and other similar aspects.

This calls for an analysis of earnings by country. Here’s an illustration:

United States

  • Generally, Machine Learning Engineers earn around $140,000 annually.
  • Machine Learning Engineers can earn up to $180,000 per year.
  • Machine Learning Engineers can expect to receive a minimum annual salary of $110,000.


  • On average, specialised Machine Learning Engineers earn $135,000 per year.
  • Engineers with specialised proficiency in Machine Learning may earn up to $185,000 annually.
  • Machine Learning Engineers can anticipate receiving a minimum salary of $98,710.

United Kingdom

  • On average, specialised Machine Learning Engineers earn £60,000 per year.
  • Machine Learning Engineers can earn up to £80,000 annually.
  • Machine Learning Engineers can expect a starting salary of £49,600.


  • Machine Learning Engineers typically make about $1.4 million per year.
  • The maximum annual salary for a Machine Learning Engineer is $3 million.
  • The minimum salary for a Machine Learning Engineer is $900,000.

Frequently Asked Interview Questions for Machine Learning Engineers

It is common practice to conduct several interview rounds while hiring Machine Learning Engineers. Typically, the first assessment comprises a general knowledge quiz, which is followed by a practical test. The practical exam often involves solving a Machine Learning problem. Those who succeed in the initial tests are usually invited for an interview to determine their suitability for the position. The initial interview questions generally cover fundamental topics, such as the definition of Machine Learning and how it operates. Here are some examples of such questions:

  1. The relationship between bias and variation requires explanation.
  2. Explain the various applications of machine learning.
  3. Explain the process of pruning a decision tree.
  4. What are the optimal uses for classification and regression?
  5. How can Bayes’ Theorem be applied to AI?

The questions mentioned above are intended to evaluate a prospective candidate’s practical understanding of machine learning engineering. To further assess their knowledge and skills, it may be necessary to request practical demonstrations along with detailed explanations. Here are some examples of such questions:

  1. A data pipeline, if you may.
  2. What method would you use to generate a decision tree?
  3. What approach would you adopt to adjust a model that has low bias but high variance?
  4. Which machine learning algorithm do you find most valuable? Demonstrate its functionality in code and clarify why it is critical.
  5. What would be your approach to develop an automated system or filter to identify and eliminate spam messages?

In numerous interviews for Machine Learning Engineer roles, use-cases are utilised to evaluate a candidate’s adaptability and flexibility. Candidates are expected to provide an overview of a plan to resolve the given problem, as opposed to presenting a complete solution. Here are some potential questions that may be asked:

  1. Perform a Principal Component Analysis (PCA) on a dataset that you know to be highly interconnected.
  2. Suppose you have been provided with an algorithm, how would you implement it in parallel using pseudocode?
  3. During an interview with a prospective Machine Learning Engineer, questions can be personalised to the individual’s specific experience and expertise within the field. Here are some possible questions: What is your background in Machine Learning Engineering or Research? What are your thoughts on the present situation of the Machine Learning industry?
  4. What measures do you take to stay updated on the most recent advancements in machine learning engineering and other relevant fields of research?
  5. Kindly discuss the machine learning engineering projects you have completed.
  6. What techniques would you employ as a Machine Learning Engineer to communicate with both technical peers (such as Data Scientists and Software Developers) and non-technical stakeholders (such as Administrators and Customers)?
  7. Can you describe a particular challenge you faced as a Machine Learning Engineer, and how you resolved it?

When working on projects as a Machine Learning Engineer, I make use of various libraries, frameworks, and packages, including TensorFlow, PyTorch, Scikit-Learn, and NumPy. Moreover, I have expertise with big data technologies, such as Apache Spark, Hadoop, and Kafka.


While some individuals may ultimately opt for a career in machine learning engineering, others may view it as just one of many potential paths to developing their technical abilities. Obtaining the necessary skills and expertise to thrive as a machine learning engineer may not necessarily follow a straight path. Thus, individuals with experience in this realm can easily switch roles to become a software developer, data analyst, or scientist. With the appropriate knowledge and background, a machine learning engineer is one of the most sought-after technical professionals in the industry, commanding a high salary and status.

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