Should I Start Learning Machine Learning Now or Later?

The decision to begin your machine learning (ML) journey now or later is a critical one, particularly given the current demand for this skill in the job market. This article explores the pros and cons of starting your ML learning now versus later, providing insights and guidance to help you make an informed decision.

Why Start Learning Machine Learning Now?

The tech world is in the midst of a golden period for machine learning. With major companies like Netflix, Google, and IBM leveraging ML, it's clear that this skill is highly sought after. Many NLP and ML startups have experienced success in recent years, contributing to the growing importance of ML in almost every industry.

Moreover, ML is the heart of artificial intelligence (AI), enabling you to create complex models that can perform a wide range of tasks. The rush to model and automate processes has led to significant advancements, making ML a powerful tool in your professional toolkit.

Why Not Start Learning Machine Learning Now?

If you aspire to become a machine learning engineer, starting now might seem like the best path. However, be aware that this is one of the most challenging roles to attain. Entry-level jobs in ML typically require extensive experience, often 3-5 years in IT. With the number of machine learning engineer (MLE) positions worldwide at over 300,000, most openings remain unfilled due to a lack of basic technical skills.

If you're a beginner with no prior experiences in technology, you might need to start at a more accessible role. For instance, data analysts serve as a stepping stone where you can gain valuable skills and experience before progressing toward MLE. As you transition into various roles within data science, such as data engineer, you'll also be nurturing your interest in ML.

Exploring Data Engineer Roles

Interestingly, the title of 'Data Engineer' is often the most coveted in the entire IT industry. Data engineers play a pivotal role in analyzing, processing, and storing vast amounts of data, making them highly valued professionals. While the path may be challenging, the rewards are significant.

The Role of Model Democratization and Automation

In the realm of machine learning, the concept of model democratization and automation has gained significant traction. As Andrew Ng noted, many of the world's models have already been developed and are in place. However, the challenge lies in making these models accessible and efficient.

Your role as a data engineer can involve extensive data cleaning and preparation, which forms a crucial part of the ML process. Although model development may not be your immediate focus, understanding these foundational aspects can provide a strong foundation for your future endeavors in ML.

Check out this video (example link) to explore how automation is evolving in the tech world. This could change your perspective and highlight the importance of foundational skills in data engineering before delving deeper into ML.

Setting Realistic Expectations

While the path to becoming a machine learning engineer is challenging, it is not impossible. Setting realistic expectations is crucial. For beginners, starting at a more accessible role, such as a data analyst or data engineer, can provide a solid foundation and pave the way for a successful career in ML.

Your journey to becoming an ML engineer involves more than just technical skills; it requires dedication and persistence. Keep learning, stay curious, and be prepared to face challenges head-on. With the right mindset and a clear understanding of the industry landscape, you can make your dream a reality.

Conclusion

Whether you start learning machine learning now or wait, the key is to stay informed about the latest trends and technologies. The demand for skilled professionals in ML is only set to grow, making it an incredibly rewarding field to explore. So, regardless of your starting point, the door is open to those who are willing to learn and adapt.