Is Machine Learning A Good Career Choice?

We’re in the gold rush of machine learning and artificial intelligence (AI), and the future looks bright for anyone pursuing an education in this quickly growing field. Over 80% of companies say they need employees who have machine learning skills, making it one of the most promising career paths.

Whether you’re naturally fascinated by data science, or you’ve simply heard the buzz about artificial intelligence, this article will help you explore the opportunities machine learning offers, equipping you to carve out a career path in an exciting and rapidly growing field.

Benefits of a machine learning career

Plenty of benefits await those who pursue a machine learning career, including diverse job opportunities and lucrative salaries. In 2021, Canada ranked fifth among 29 countries for its AI research, development, and economic impact. The Canadian government has also invested heavily in efforts to drive AI adoption through its Pan-Canadian Artificial Intelligence Strategy, underscoring the nation’s commitment to developing AI and machine learning. If you’re considering a new career or an educational path, now is the time to seize opportunities to learn in this burgeoning industry.

High demand and low supply

Demand is outpacing supply in machine learning, with the need for employees with relevant skills expected to grow by 71% between 2020 and 2025. As the industry expands, all sorts of companies are seeking skilled professionals to serve as system architects, weaving together algorithms and data to create models capable of learning and evolving independently. These professionals’ expertise lies in designing and implementing machine learning algorithms, selecting and preprocessing data for analysis, and fine-tuning models for optimal performance. They possess a deep understanding of statistical concepts, programming languages, and frameworks, allowing them to navigate intricate data patterns. Whether predicting consumer behaviour, optimizing processes, providing customer support, or unlocking insights from vast datasets, a skilled machine learning professional navigates vast amounts of information. Ultimately, they drive innovative solutions and uncover hidden potential within the data-driven world.

Competitive salaries

The surge in demand for machine learning experts translates into competitive salaries, making machine learning engineers among the highest-paid professionals in the tech industry. Glassdoor reports salary ranges for various types of career paths where machine learning can play a role, including:

• Data scientist: CA $80K–$124K
• Deep learning engineer: CA $59K-126K
• Machine learning engineer: CA $78K-118K

If the prospect of joining a flourishing industry with growth opportunities appeals to you, a career in machine learning is an excellent place to start, offering immediate financial rewards and the potential for continued growth as the field expands.

Challenges in a machine learning career

Although machine learning is an exciting field, it has challenges. In an industry that’s evolving rapidly, you must stay on top of the trends and be open to ongoing education. Preparing for the pitfalls can help you navigate the road ahead.

Young industry that’s continuously changing

Machine learning is a relatively young industry, and professionals must adapt to ongoing changes in algorithms, frameworks, and tools. This constant evolution demands a commitment to lifelong learning, requiring individuals to stay updated on the latest advancements in the field.

Unlike roles with more clearly defined parameters, machine learning experts may fit into various organizational roles to fulfill specific functions. Machine learning engineer Cristiano Breuel highlights machine learning applications currently in use commercially, demonstrating the dynamic nature of a field that requires continuous learning. Depending on their chosen industry, those pursuing a machine learning career will need to stay up to date on developments and tools used in:

• Customer profile analysis, for example to make decisions about granting credit or prioritize marketing actions.
• Recommendation systems for products, services and content.
• Fraud detection systems.
• Demand forecasting and logistics optimization.
• Natural language analysis for routing or automatic response (chatbots).
• Image analysis for object and face detection.
• Text recognition and document structure extraction from images.
• Speech to text transcription.
• Translation between languages.
• Screening for medical imaging exams.

Data scientist, machine learning/machine ops engineer, analyst, and machine learning researcher are just a few potential roles where an updated knowledge of machine learning is crucial.

Lack of infrastructure and resources

Companies of all sizes may struggle to provide the necessary infrastructure and resources for machine learning projects. Even larger organizations with the resources to implement machine learning initiatives may face challenges in adopting agile machine learning practices due to existing structures and processes. One study on the adoption of generative AI at Fortune 1000 organizations found that 59% of organizations lack the resources to meet generative AI innovation expectations. This lack of resources can hinder professionals’ efficiency, underscoring the importance of choosing employers committed to supporting machine learning initiatives.

Difficulty in breaking into the field

The demand for skilled machine learning professionals is on a continuous upswing, yet the path to breaking into machine learning has its hurdles. The scarcity of practical experience can be one such roadblock. Machine learning thrives on hands-on expertise, and breaking into the field can be challenging without a substantial portfolio or relevant projects. As technologies evolve and industry expectations soar, aspiring professionals must showcase their problem-solving skills and ability to create and implement machine learning solutions. Pursuing education in data science can help in developing skills that may be required for a career in machine learning, like:

• Programming proficiency: Successful implementation of machine learning algorithms requires a comprehensive grasp of programming languages like Python, Julia, and other advanced programming languages.
• Deep learning knowledge: Deep learning, a subset of machine learning, necessitates a solid foundation in mathematics and statistics to complement your programming skills.
• Mathematical aptitude: You must be adept at probability, statistics, linear algebra, and other mathematical concepts to grasp machine learning algorithms. Applying these principles in the context of machine learning requires deep knowledge beyond the basics.
• Distributed computing knowledge: To scale machine learning algorithms effectively, you must understand distributed computing. Software engineering and cloud computing knowledge are crucial, as algorithms are distributed across numerous computers during the training process.
• Algorithmic complexity: Machine learning algorithms, known for their intricacy, can pose challenges for beginners. Each algorithm has various components you’ll need to understand before application. Moreover, not all algorithms fit every dataset or business problem, necessitating experimentation to discover the optimal approach.

It should also be noted that landing a machine learning career usually requires an advanced level of training and experience, so be prepared to potentially work your way up through various machine learning aligned jobs before entering the field.

Is machine learning in demand in Canada?

With the rapid growth of the Canadian tech industry in Canada, machine learning professionals have ample opportunities for career advancement. Companies are actively seeking individuals with machine learning expertise, providing a clear path for career progression.

Statista reports the Canadian machine learning market is expected to achieve a market size of over CA $5.25 billion in 2023, with a projected annual growth rate of 17.16% — boosting the market volume to CA $15.93 billion by 2030.

Emerging trends include increased adoption of AI in utility, finance, insurance, and information and culture companies in Canada. Meanwhile, construction, food, and accommodations may be slower to adopt such technologies. With the growth of the Canadian machine learning market, however, it’s crucial to stay abreast of evolving trends, as all industries are ripe with opportunity. Some of the top in-demand jobs in Canada include roles such as:

• Machine learning engineer: Machine learning engineers are in high demand among Canadian tech recruiters. These engineers can flourish in Canada's technologically rich environment, offering growth opportunities in established companies and emerging startups.
• Computer vision engineer: With tasks encompassing image analysis and AI implementation. These engineers extract data and employ machine learning to enable computers to interpret visuals. Expect to see applications in healthcare (skin cancer detection), automotive (accident prevention), and manufacturing (workplace safety).
• Various tech roles: Security engineers, data engineers, data science specialists, product designers, and other tech professionals have an opportunity to amplify their skill sets to include machine learning.

Many of these roles are also remote-friendly, as remote hiring is rising in Canada. Since 2016, there’s been nearly an 800% increase in remote employees, with data scientists, machine learning engineers, UX researchers, and other tech roles included in this group.

How long does it take to learn machine learning?

For those aspiring to enter the machine learning field quickly, bootcamp-style learning has gained popularity.

Platforms like Lighthouse Labs offer intensive, immersive programs designed to equip individuals with practical data science and machine learning skills in a relatively short time frame. The program teaches transferable skills and how to adapt to a changing tech landscape, setting graduates up for long-term success as they navigate their way to a machine learning career.

The Data Science Program provides immersive education with flexibility for aspiring machine learning professionals. With personalized career services support, our team can help you land the right data science roles to set you on the path to your dream machine learning career.

The curriculum covers programming, math foundations, data analysis, and more, offering two study options: a 12-week full-time bootcamp with five weekly lectures or a flexible 30-week program with two weekly lectures. Here are some program highlights:

• The program enables students to gain a diploma and essential skills in data science pillars such as programming, data tools, data soft skills, and data analysis.
• The admission process involves an online application, interview, logic and technical tests, and prep modules.
• Lighthouse Labs provides comprehensive support, including on-demand mentorship, proactive student support, and lifelong career assistance.

Learn more about the Lighthouse Labs’ Online Data Science Program here.

Does machine learning require coding?

Yes, proficiency in programming languages such as Python, R, C++, and JavaScript is essential for implementing machine learning algorithms and building robust models. However, Lighthouse Labs’ courses and programs can help you learn the fundamentals without prior coding knowledge.

Skill overlap and transferability

Skills acquired in machine learning often overlap with those in data science, artificial intelligence, web development, and traditional software engineering. This overlap enhances the transferability of skills, allowing professionals to transition between these fields seamlessly.

Want to learn more?

Navigating the field of machine learning requires a diverse skill set, encompassing computer science, statistics, domain-specific knowledge, and a nuanced understanding of ethical considerations. Aspiring professionals must recognize the importance of continuous learning, especially in the dynamic landscape of deep learning techniques.

Beyond technical expertise, a holistic approach that incorporates insights from humanities disciplines like psychology and philosophy becomes increasingly valuable as technology continues to shape human experiences.

If you're inspired by the vast potential of machine learning, consider taking the next step by exploring potential career path options.

This article is brought to as part of ICT Boost initiative, funded by the Government of Canada's Sectoral Workforce Solutions Program. The ICT Boost Initiative is now full and no longer accepting applications for fully funded programs.