Should You Pursue Web Development or Machine Learning in 2024?

Web Development or Machine Learning: As technology advances, exciting new career paths are emerging. Two red-hot fields today are web development and machine learning.

But which one is right for you? Should you focus on mastering HTML, CSS and JavaScript? Or statistics, data mining and neural networks?

Both offer great opportunities. Let’s dive in and compare key factors to help you decide between pursuing web development or machine learning in 2024.

What Each Career Path Involves

First, what types of work make up each field day-to-day?

Web Development

Web developers design, build and implement websites and web applications. Core activities include:

  • Writing semantic HTML, CSS and JavaScript code.
  • Developing both server and client-side application logic.
  • Integrating APIs and databases into web apps.
  • Ensuring sites are responsive, accessible and optimized for browsers.
  • Translating UI/UX design mockups into functional front-end code.
  • Collaborating with designers, product managers and back-end developers.
  • Deploying apps on servers or platforms like AWS.
  • Continuously deploying updates and new features.

Machine Learning Engineering

Machine learning engineers develop and deploy complex AI-powered systems. Their work includes:

  • Collecting, cleaning and labeling huge datasets.
  • Conducting exploratory data analysis to surface insights.
  • Training and fine-tuning machine learning models like neural networks.
  • Developing pipelines to feed real-time data into models.
  • Serving models at scale on cloud infrastructure.
  • Monitoring models in production and retraining when needed.
  • Using statistics and calculus to improve model performance.
  • Staying up-to-date on new techniques like deep learning.

As you can see, the day-to-day work differs significantly between the two careers.

Required Skills and Knowledge

The technical skills needed for each role are also quite different.

For web development, you’ll need proficiency in:

  • HTML to structure content.
  • CSS for styling and layout.
  • JavaScript to add interactivity and build web apps.
  • Frameworks like React, Angular, Vue for complex front-ends.
  • Server-side languages like PHP, Python, Node.js.
  • Relational databases like MySQL and NoSQL databases.
  • Git version control system.
  • REST APIs for integrating with backends.
  • Responsive design principles.
  • Web performance optimization.
  • Fundamentals of UI/UX design.

For machine learning engineering, important skills include:

  • Probability theory and statistics.
  • Linear algebra and multivariate calculus.
  • Data mining techniques like regression and classification.
  • Machine learning algorithms like neural networks.
  • Libraries like TensorFlow, PyTorch, Keras and Scikit-Learn.
  • Data pipeline development tools like Apache Airflow.
  • Cloud platforms like AWS, GCP for model deployment.
  • Python and SQL programming languages.
  • Experiment design and A/B testing.

As you can see, while there is some overlap in general programming skills, the required knowledge bases are largely distinct.

Learning Resources Available

When starting out, having quality educational materials is crucial for building skills quickly.

Fortunately, there are abundant learning resources available for both fields:

Web Development

  • Interactive coding platforms like FreeCodeCamp, Codecademy.
  • In-depth video courses on platforms like Udemy, Pluralsight.
  • Coding bootcamps – both in-person and online.
  • University computer science programs.
  • Massive open online courses (MOOCs).
  • Official language documentation hubs like MDN Web Docs.
  • Hands-on project ideas from sites like Frontend Mentor.

Machine Learning

  • University computer science/data science programs.
  • MOOCs focused on AI from Coursera, edX.
  • Online video courses tailored to industry.
  • Bootcamps specializing in data science.
  • Official framework docs like TensorFlow.
  • Data competitions like Kaggle.
  • Q&A sites like Cross Validated.
  • Machine learning blogs and tutorials.

The technology learning ecosystem has expanded massively over the past decade for both fields. There are now diverse, high-quality resources available online to get started and advance your skills.

Career Trajectory and Growth

What do the career paths look like long-term?

For web developers, typical trajectories include:

  • Junior developer → Mid-level / Senior developer → Tech lead
  • Specializing in front-end → back-end → full-stack development
  • Moving into related roles like UX/UI design, product management or engineering management
  • Starting as an employee → becoming a freelance web developer
  • Progressing from smaller companies to big tech firms like Google, Amazon, etc.

For machine learning engineers, common career advancements include:

  • Junior ML engineer → Senior ML engineer → ML architect
  • Generalist → specializing in areas like NLP, computer vision, recommender systems
  • Leading an ML team or managing large ML projects
  • Becoming an ML researcher developing new algorithms
  • Starting an ML/data science focused startup
  • Shifting into adjacent roles like data analyst, data engineer or data architect

The career growth tends to branch out in different directions between the two fields. Both offer opportunities to take on leadership roles or specialize further.

Compensation Potential

Of course, pay is an important consideration too!

According to Glassdoor, average base salaries in the United States are:

  • Web developer – $76,539 / year
  • Machine learning engineer – $121,466 / year

However, experienced professionals can earn significantly higher, especially in tech hubs like Silicon Valley.

While pay varies significantly by location and experience, machine learning engineers tend to earn higher salaries – especially as demand grows for experts in AI.

Job Satisfaction and Interests

Job satisfaction also depends heavily on your natural strengths and passions.

Web development suits those who enjoy:

  • Constantly learning new frameworks and languages
  • Logical, detail-oriented work
  • Creativity and visual design
  • Building user experiences step-by-step
  • Rapidly evolving technology

Machine learning engineering appeals to those who like:

  • Mathematics, statistics and data analysis
  • Optimizing complex systems end-to-end
  • Writing efficient algorithms
  • Conducting quantitative experiments
  • Drawing insights from data

So focus on which aspects naturally energize you when choosing between the career paths.

Job Market Outlook

Finally, let’s look at employment projections. The US Bureau of Labor Statistics forecasts:

  • Web developer jobs to grow 13% from 2020 to 2030 – faster than average. About 167,000 new jobs will open up.
  • Machine learning engineer jobs to grow 36% over the same period – much faster than average. Around 21,000 new jobs will open up.

While machine learning is growing faster as a field, there will be far more total web developer roles available.

Overall, both career paths have abundant opportunities. Machine learning is growing rapidly but from a smaller base, while web development represents a larger, more established field.

So which should you pursue? Evaluate your own strengths, passions and professional goals. Both fields allow the chance to work on impactful, exciting technologies and continue learning throughout your career. Can’t go wrong with either option!

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