How To Become A Machine Learning Engineer In Australia?

Are you an analytical person with experience in computer science and mathematics? If yes, a Machine Learning Engineer career could be perfect for you.

Machine learning engineers explore, mine, analyze data and retrieve valuable patterns to develop or strengthen machine learning models. They identify ways to improve customer service through machine learning technology.

Most Employers seek individuals with a VET qualification or a university degree In Data Science, Computer Science, Information Technology, Engineering (Software Engineering), or a related field to work in this profession.

This blog is a helpful guide for those aspiring to become Machine Learning engineers. It discusses what skills you need to succeed, what pay levels to expect at each stage on the career path, how to advance your career, and finally, how to become a Machine Learning Engineer in Australia.

1. What Is Machine Learning?

Machine learning is a field of computer science concerned with the application of Artificial Intelligence (AI). It uses algorithms that interpret data to understand consumers' preferences and patterns.

Machine learning revolves more around entering smart and relevant data into machines. It trains the machine to learn to do tasks smartly using algorithms.

Technological evolution has created opportunities for better and more extensive applications. It has led to the rapid inclusion of machine learning in various industries such as Social-Media, Google ecosystems, Ecommerce, Digital or web security, and transportation to perform several tasks such as:

• Video surveillance 

• Facial recognition on smartphones

• Designing ads and targeting them at users based on their likes, posts, and preferences for the website

• Suggesting items to purchase based on the customer's viewing history and purchases 

• Mapping and Navigation systems

• Self-driven cars

• Virtual assistants

2. What Is A Machine Learning Engineer?

A Machine Learning Engineer is a software engineer specializing in machine learning. They are vital in designing and developing AI-driven algorithms to function and solve issues independently.

These experts leverage their skills to automate routine and manual tasks to improve productivity and operational efficiency in a business.

3. Machine Learning Engineer Duties

The main job of a Machine learning Engineer is to analyze large data sets and find meaningful connections and patterns. A typical day at work comprises the following tasks and duties:

• Perform statistical analysis and implement machine learning algorithms 

• Design and develop reliable performing machine learning systems

• Run Artificial Intelligence systems experiments and tests

• Research, design, program, and deploy the AI for machine learning and enhancing and maintaining current AI systems. 

• Develop web services or APIs to expand machine learning libraries for stakeholders, users, and internal teams.

• Train machine learning systems when needed.

• Translate the machine learning models to analytic applications.

• Liaise with the data scientists who build the models for AI systems and those who run them.

4. What Skills Do I Need For Machine Learning?

To become a machine learning engineer, you need a blend of math, statistics, computer science, programming languages, and web development skills. Knowledge of these concepts will prepare you to apply for machine-learning Engineering jobs.

Here is the complete list of machine learning engineer skills:

• Strong fundamental knowledge of computer science fundamentals - computer architecture, searching and sorting algorithms, and software development and testing.

• Experience building data structures such as maps, sets, dictionaries, binary trees, linked lists, and prefix trees.

• Able to organize, analyze, and measure large data sets

• Knowledge of current industrial-level coding languages such as Python, Java, R, Clojure, Scala, and C++ and operating systems such as Windows, Linux, and Mac

• Knowledge of the machine learning APIs, libraries, frameworks, packages, and tools such as Keras, Pandas, Tensorflow, Sci-Py, Sci-Kit, Num-Py, Spark MLib, H2O, Theano, etc

• Keeping up with developments in the field is necessary so they can take advantage of the latest technologies.

• Probability, mathematics, and statistics knowledge, such as Bayes net, Conditional Probability, Types of Distribution, Hidden Markov Models, Hypothesis testing, ANOVA, etc.

5. What Qualifications Do I Need To Be A Machine Learning Engineer?

There is no minimum qualification to work as a Machine Learning Engineer in Australia. However, undertaking relevant courses can help you develop much-needed knowledge and skills and improve your entry into this profession.

1. Complete your Year 12 education with one or more pre-requisite subjects such as English, math, science, and statistics.

2. Consider earning a relevant degree course from a recognized university. Preferred study areas include:

• Data Science

• Computer Science

• Information Technology

• Engineering (Software Engineering)

The above courses will help you lay a strong foundation in computer architecture, programming languages, data structures, software development, and other areas necessary to work in this role.

3. Consider enrolling in a machine learning engineering course or a certification program to learn to apply machine learning algorithms to real-world business problems.

4. Gain work experience and develop a diverse Machine Learning Portfolio by completing Machine Learning internships, working on Machine Learning Projects, or getting into any of the below relevant entry-level positions:

• Computer Engineer

• Software engineer

• Data scientist

• Software Developer

6. Steps To Become A Machine Learning Engineer In Australia

Here are the steps you need to follow to get started in this role:

Step 1: Obtain A Relevant Degree

A machine learning engineer must have a good grasp of mathematics, statistics, computer, data science, and programming.

Completing an undergraduate degree in one of these disciplines is the ideal route to building the required knowledge and job skills. 

Step 2: Build Programming Knowledge

Proficiency in programming languages is another requirement to work in this role. Machine learning engineers use popular programming languages: Python, R, Java, C++, Scala, Julia, and MATLAB.

Step 3: Study Machine Learning

Along with the knowledge of computer science and programming languages, you need a good understanding of the essential machine learning tools, frameworks, models, libraries, and technologies to prepare yourself to work in a machine learning career.

Google's TensorFlow is an excellent framework for writing in Python, C++, Swift, and Java and performing various deep-learning tasks, like speech and image recognition.

PyTorch is another framework for beginners with the imperative programming model designed to use standard Python statements and implement deep neural networks.

Familiarize yourself with machine learning engineers' most commonly used tools, such as Spark, Apache Kafka, and TensorFlow.

Step 4: Enroll in a Machine Learning Course

It is easier to break into machine learning with a machine learning certification program, an online, or a Bootcamp course. There are many courses available on Coursera, Udacity, or Edx that you can take to gain knowledge in Machine Learning.

These courses teach students how to use data, choose the relevant machine-learning model, and apply machine-learning algorithms to real-world business problems.

In addition to the courses, you can also refer to dozens of other learning materials in the form of podcasts, video sessions, and articles available on the internet.

Step 5: Build your Machine Learning Portfolio

Once you have the theoretical Machine Learning knowledge, the next thing is to gain practical exposure through real projects on real data. One of the best ways to gain experience is to enrol in a machine learning internship.

Another way is to enter an entry-level role as a Software Engineer and gain experience. Work your way up to get into a "Junior Machine Learning Engineer" role to build experience in Machine Learning. As you earn more experience and expertise, consider progressing to a senior position in Machine Learning.

If you need help finding an internship, or an ML job, you can work on personal projects to gain practical Machine Learning experience. Review and recreate basic projects available at PredictionIO, ProjectPro, Scikit-learn, Awesome Machine Learning, and similar resources. 

Kaggle is the best place to find and solve real-world problems. Quandl and UCI Machine Learning Repository are the best places to use publicly available data sets.

With good practical hands-on experience in machine learning, start creating your projects and adding them to your work portfolio. GitHub is a great place to come across project ideas to design your first Machine Learning project. 

Build a job-winning online portfolio by including diverse machine-learning projects in the following areas:

• NLP 

• Distributed Computing

• Neural Networks

• Data Modelling and Evaluation

• Reinforcement Learning 

• Machine learning tools and technologies such as Python, TensorFlow, R, Keras, etc.

Step 6: Earn a Machine Learning Certification

A Professional Certification is a valuable asset to show your Machine Learning expertise to potential employers. It proves that you have the skills and knowledge that meet specific standards. 

Some of the most recognized certifications in Machine Learning include:

• Professional Machine Learning Engineer- Google Cloud Platform

• AWS Certified Machine Learning - Specialty - Amazon Web Service

• Machine Learning Professional Certificate - IBM

7. How Much Do Machine Learning Engineers Make In Australia?

A career in Machine Learning is highly lucrative. Even entry-level Machine Learning Engineers are handsomely rewarded. According to PayScale, the average Machine Learning Engineer's salary is AU$ 77,677 per year or an hourly equivalent of AU$ 45.00.

Salary in Australia Based on Experience

• Entry-level Machine Learning jobs (<1-year experience): AU$67,893 per year 

• Early career Machine Learning jobs (1-4 years of experience): AU$75,883 per year 

• Mid-career Machine Learning jobs (5-9 years of experience): AU$125,126 per year 

Salary Based on Location

• Melbourne - $123,840 per year

• Sydney - $137,879 per year

• Perth - $106,147 per year

• Brisbane - $102,538 per year

• Canberra - $99,205 per year

• Clayton - $92,047 per year

• Docklands - $91,185 per year

8. Job Prospects Of Machine Learning Engineers In Australia

Machine learning is everywhere in our lives. Everything we do today depends on machine learning - reading and replying to emails, scrolling through social media news feed, watching movies on Netflix, buying an item on Amazon, and chatting with virtual assistants.

Behind all these technologies is the hard work of Machine Learning Engineers who build and deploy smart applications and maintain them to ensure they work flawlessly.

The crucial role that Machine Learning plays in today's data-driven world makes it a high-demand sector across several industries, such as Financial and Insurance Services, Public Administration and Safety, and Professional, Scientific, and Technical Services.

As more and more consumers spend more time and money online, the need for enhanced automation of repeated and routine tasks will continue to rise. The government projects a strong job outlook with a steady growth of machine learning jobs in Australia.

Presently, Victoria and New South Wales have the largest share of machine learning engineers.

9. Frequently Asked Questions (FAQs)

How To Become A Machine Learning Engineer Without A Degree?

To get a Machine Learning Engineer position, you will need a background in math, statistics, computer science, computer programming, software engineering, data structures, or machine learning algorithms.

If you want to avoid completing a degree course, consider taking machine learning courses/training programs and work experience to build niche expertise in machine learning.

This way, you can develop foundational skills on how machine learning works and how to use real data and apply machine learning to solve real business problems in the workplace.

Thus, to enter a Machine Learning job without a formal degree course, you would need a good amount of work experience and a strong and diverse machine learning portfolio to demonstrate your competency to potential employers and stand out from other job applicants for this position.

How Is A Machine Learning Engineer Different From A Data Scientist?

Both machine learning engineers and data scientists are vital throughout the life cycle of a big data project.

They work closely to realize a fast and effective delivery of business value. However, when it comes to choosing one, a machine learning engineer vs a data scientist comparison is beneficial.

Data science is much broader than machine learning. An ML engineer's role is the subset of a data scientist's position. Here is how they differ from each other: 

• An ML engineer serves as a bridge between the model-developing task of the data scientist and the development of robust machine learning systems and services that are ready for production.

• A data scientist produces meaningful insights in the form of charts, or reports, while a machine learning engineer builds self-running software to automate machine learning models. 

• A Machine Learning Engineer uses a variety of machine learning models to make estimates based on past data. A Data Scientist, on the other hand, deals with complex real-world data to develop actionable insights. 

• Where a Machine Learning Engineer is purely experimental, the role of a Data Scientist is investigative.

• Professionals in both these fields require distinct skillset. ML Engineers need basic Math and Statistics concepts, ML algorithms, PyTorch, NLP, Deep Learning, Python, Tensorflow, Keras, Data Wrangling, APIs, Algorithm Deployment, and Scaling on Cloud. Skills for Data Scientists include Statistical Skills, Big Data platforms, Machine Learning, Programming Languages, and Data Visualization.

• Salary is another factor that differentiates both professions. The annual average pay of an ML Engineer is AU$ 77,677 per year. A Data Scientist earns AU$91,600 per year on average. 

If you like the technical aspect of developing software models and want to contribute to the evolution of artificial algorithms, then ML is an ideal match for your skills.

How Hard Is It To Become A Machine Learning Engineer?

For most students, intense mathematics is the most challenging aspect of becoming a machine learning engineer. Furthermore, learning and debugging machine learning algorithms and identifying why they do not work as well makes it somewhat scary for beginners.

Moreover, to get into this role, you need to be highly creative, experimental, and tenacious. However, with adequate availability of academic resources, it has become easier and quicker to develop skills and knowledge to work in this role.

How Long Does It Take To Become A Machine Learning Engineer?

The ideal duration of becoming a Machine Learning Engineer depends on the course, study mode (Full-time/Part-time), and if you are taking relevant certifications. Generally, it can take 1-3 years of study and work experience in Machine Learning to become a Machine Learning Engineer.

If you want to know how to become a machine learning engineer at Google, the following courses can help you achieve your professional goals.

• Certified Artificial Intelligence Professional – AIICT, six months, Online

• ICT40120 - Certificate IV in Information Technology (Networking) – AIICT, 15 months, Online. You can also do this course at TAFE NSW.

• ICT50118 – Diploma of Information Technology – Western Sydney University, 1-year full-time 

• Certified Data Science Professional- AIICT, 3-5 months, Online

• Certified Microsoft Full Stack Developer- AIICT, 4-6 months, Online

• Machine Learning with Python – Coursera – Online, approx. 23 hours to complete, earn a Certificate upon completion

• Deep Learning Specialization — Coursera - Online, approx. Five months to complete, and earn a Certificate upon completion.

• Advanced Machine Learning Specialization — Coursera - Online, approx. Three months to complete, and earn a Certificate upon completion.

Machine Learning Degrees In Australia

• Bachelor of Science (Mathematics) – The University of Southern Queensland

• Bachelor of Computer Science (Artificial Intelligence) - University of Wollongong

• Bachelor of Computer Science - Deakin University

• Bachelor of Computer Science (Data Science) – Swinburne University of Technology

• Bachelor of Computer Science (Artificial Intelligence and Big Data)

• Bachelor of Software Engineering (Artificial Intelligence) - Torrens University Australia

Machine Learning Master's Degrees Courses In Australia

• Master of Computer Science (Machine Learning and Big Data) - University of Wollongong

• Master of Information Technology (Artificial Intelligence) - Macquarie University

• Master of Artificial Intelligence – RMIT 

• Master of Artificial Intelligence and Machine Learning – The University of Adelaide

What Jobs Can I Get In Machine Learning?

According to job openings for Machine Learning Engineers on SEEK, people who specialize in machine learning can work in several different titles and jobs, including:

• Machine Learning Engineer

• Data Scientist

• Data Engineer

• Machine Learning Researcher

• Software Developer/Engineer

• NLP Scientist

• Human-Centred Machine Learning Designer

• Business Intelligence Developer

Is ML Engineering A Good Career?

Since industries working with large data sets have started realizing the importance of machine learning technology to obtain valuable insights from the data in real-time, it helps them work more efficiently and gain a competitive edge.

As a Machine learning Engineer, you use algorithms to build models that help organizations form better decisions without human intervention.

There is a growing demand for these professionals in various industries, such as:

• In the financial sector (identify investment opportunities, find and prevent fraud and minimize identity theft), 

• Retail (obtain customer insights, create targeted ads, and provide a personalized shopping experience)

• The oil and gas industry (to identify new energy sources)

• Transportation industry (making truck routes more efficient)

• The automotive industry (for self-driving cars)

• Government agencies such as public safety and utilities. 

• Healthcare industry (the advent of sensors and wearable devices and sensors for real-time health assessment) 

Thus, from manufacturing to health care, each field is gradually adopting ML techniques and tools to advance to the next level and ensure error-free functioning. 

Currently, the field is in evolving stage, and there are immense shortages of educated graduates with the appropriate skills. The demand for machine learning will skyrocket in the coming years leading to an abundance of full-time employment opportunities for capable candidates.

10. Conclusion

Machine Learning is an in-demand profession that opens the door to numerous learning and career advancement opportunities. 

This blog has given you an overview of the Machine Learning Engineer profession and what you need to do for a quick start in this profession.

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