Come sono diventato MACHINE LEARNING ENGINEER in GLOVO | Guida Step By Step

Summary

The video script discusses the transition from a data scientist to a machine learning engineer, offering advice for those considering a similar career change. It emphasizes the importance of understanding business problems, translating them into solvable AI challenges, and managing the machine learning model lifecycle. The speaker shares personal experiences and recommends focusing on behavioral, statistical, machine learning, system design, coding challenges, and SQL skills to succeed in interviews and the role itself.

Takeaways

• 🎓 The speaker transitioned from a data scientist to a machine learning engineer and shares insights on making a similar career change.
• 📈 The role of a machine learning engineer varies greatly depending on the company they work for, but generally involves addressing business problems using AI and machine learning techniques.
• 🤖 A machine learning engineer communicates with stakeholders to understand business problems and translates them into solvable AI problems.
• 🛠️ The engineer is responsible for deploying models, monitoring their performance, and ensuring they meet predefined constraints.
• 📊 Key competencies for a machine learning engineer include knowledge of statistics, machine learning, deep learning, and MLOps, as well as proficiency in Python and R.
• 🧠 Interview preparation involves focusing on behavioral questions, showcasing how you approach work, handle projects, and manage deadlines and conflicts.
• 📚 For technical interviews, expect questions on statistics, machine learning, deep learning, system design, and coding challenges.
• 💡 The speaker emphasizes the importance of practice, suggesting mock interviews and problem-solving exercises to improve interview skills.
• 📈 The speaker recommends several resources for learning and preparation, including books like 'Hands-On Machine Learning with Scikit-Learn' and 'Deep Learning' by Goodfellow et al.
• 🔍 Understanding the trade-offs between different models and knowing when to use each is crucial for a machine learning engineer.
• 🚀 The journey to becoming a machine learning engineer is challenging and requires dedication, discipline, and continuous learning.

Q & A

What is the main transition discussed in the transcript?

The main transition discussed is from working as a data scientist for a mobile gaming company to becoming a machine learning engineer.

What does the speaker describe themselves as?

The speaker describes themselves as 'mediocre but ambitious', highlighting their drive to improve despite their self-assessed average abilities.

What is the role of a machine learning engineer according to the speaker?

According to the speaker, a machine learning engineer is someone who communicates with business and product stakeholders to understand business problems and translate them into solvable AI or machine learning problems. They are also responsible for deploying models, monitoring them, and managing their lifecycle.

What are the key skills a machine learning engineer should have according to the speaker's experience?

Key skills for a machine learning engineer include statistical and probability knowledge, machine learning, deep learning, and MLOps competencies, as well as proficiency in Python, R, and other basic programming languages.

What are the six macro categories the speaker suggests focusing on to pass a machine learning engineer interview?

The six macro categories are behavioral phase, statistics, machine learning and deep learning, machine learning system design, code challenges, and SQL.

How does the speaker suggest practicing for behavioral interview questions?

The speaker suggests finding 15-30 behavioral questions online, writing down answers, and practicing responses using the STAR (Situation, Task, Action, Result) method. They also recommend conducting mock interviews with non-ideal companies to develop interview skills.

What book does the speaker recommend for understanding statistical concepts?

The speaker recommends 'Trustworthy Online Controlled Experiments: Practical Guide to A/B Testing' for understanding statistical concepts and their application in business.

Which books are suggested for gaining knowledge in machine learning and deep learning?

The speaker suggests 'Hands-On Machine Learning with Scikit-Learn' and 'Deep Learning' by Goodfellow, Bengio, and Courville for gaining knowledge in machine learning and deep learning.

What resources are recommended for learning about machine learning system design?

Resources recommended for machine learning system design include 'Machine Learning System Design Interview' by Ali Ammar and 'Design Patterns for Common Machine Learning Challenges'.

How does the speaker approach code challenges during interviews?

The speaker approaches code challenges by focusing on solving the problem optimally, aiming to use minimal space and ensure high performance. They emphasize the importance of practice and understanding the underlying concepts.

What advice does the speaker give for SQL code interview preparation?

The speaker advises practicing SQL code problems, focusing on understanding and optimizing SQL queries, and being prepared to handle various SQL-related challenges during interviews.