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Essential Traits for Excelling as a Machine Learning Engineer

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Chapter 1: Introduction to Machine Learning Traits

When pursuing a goal, I always look to the best in the field and determine what it takes to reach their level. For example, to enhance my football dribbling skills, I studied Lionel Messi, the GOAT (Greatest of All Time). My approach involved not just admiring his incredible talent but also analyzing his decision-making: 1) understanding when he chooses to dribble or pass, and 2) learning how he mastered dribbling in the first place. This method has been invaluable, allowing me to emulate others as I acquire new skills.

As Isaac Newton once said, “If I have seen further it is by standing on the shoulders of Giants.”

In contrast to football, machine learning isn’t a sport. Observing top machine learning engineers solving problems isn’t as straightforward unless you collaborate with them in competitions like Kaggle or on the same team. While these scenarios are challenging, gaining insight into their thought processes is more accessible, leading to the first trait I want to highlight.

Here are three essential traits that I have observed in the most accomplished software engineers:

Section 1.1: The Power of Communication

Effective communication is a crucial skill; it fosters understanding and collaboration among individuals. Consider some renowned machine learning engineers and data scientists:

  • Have you listened to Andrew Ng explain backpropagation?
  • Have you heard Cassie Kozyrkov discuss P-values?
  • Have you read Aurelien Geron's take on a machine learning pipeline?
  • Have you seen Andriy Burkov break down the machine learning workflow?

Engaging with their work often clarifies complex topics that once felt overwhelming. Their expertise is evident, but the ability to convey their knowledge in an understandable manner is a learnable skill. It's essential to develop this skill since data teams consist of diverse roles beyond just machine learning engineers and data scientists; effective communication ensures alignment among all team members.

Section 1.2: Embracing Skepticism

Machine learning algorithms are designed to reveal the underlying structures in data, enabling generalization to new cases. However, it’s naive to assume these algorithms will always make correct decisions simply because they are crafted by intelligent individuals. This is particularly critical when outcomes can significantly impact lives, such as predicting health conditions like cancer.

As Carl Sagan noted, “I do not want to believe, I want to know.”

A skeptical mindset enables machine learning engineers and data scientists to remain objective while conducting their work. This trait encourages professionals to critically assess their claims—whether stemming from feature engineering or the model itself. Documenting outcomes, whether successful or not, is vital for understanding and improving processes.

Section 1.3: The Importance of Perseverance

Recently, I've come across numerous accounts from individuals who have attempted (some unsuccessfully) to secure positions as machine learning engineers at FAANG companies. These organizations employ some of the brightest minds in the field. Although I'm not seeking employment at any of these companies, I find their hiring processes fascinating, especially a quote from Mark Zuckerberg: “I will only hire someone to work directly for me if I would work for that person.”

According to Rahul Agarwal, the hiring process at Facebook typically includes:

  1. Phone Screening
  2. Onsite Coding Round
  3. Onsite Coding Round 2
  4. System Design (including ML system design)
  5. Behavioral Interview

FAANG companies aim to filter out candidates who may not align with their standards, which makes sense given the high costs associated with hiring the wrong engineer. This rigorous interview process offers a deeper insight into a candidate's perseverance and leadership qualities.

Throughout your machine learning journey, perseverance is essential. Keeping abreast of technological advancements requires dedication. You will face failures—often! Since identifying the best model before analyzing data is impossible, numerous less-than-ideal experiments are necessary to gain a better understanding and build an effective model. While failure is uncomfortable, it's often a prerequisite for success.

Final Thoughts

Technical skills are a given for a machine learning engineer; however, honing additional abilities that enhance one’s character can set you apart. A straightforward way for machine learning engineers to distinguish themselves is by cultivating leadership skills that make them more noticeable in their field.

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