Join us for a fireside chat with Santiago Valdarrama, a machine learning engineer, educator, and freelancer, renowned for his hands-on, pragmatic approach to AI and ML. Hugo Bowne-Anderson will host this Outerbounds event, diving into the real-world challenges and opportunities of implementing machine learning at scale.
Santiago, creator of a highly acclaimed end-to-end machine learning course, is dedicated to equipping engineers with the practical skills needed to excel in real-world ML environments. His expertise in simplifying complex concepts and preparing students for real-world challenges offers invaluable insights for ML practitioners at all levels.
Key topics of discussion:
– Full Machine Learning Lifecycle: How to master the entire process from data collection to deployment and monitoring.
– ML in Production: Overcoming common pitfalls in deploying machine learning models.
– AI/ML Evolution: What sets modern AI approaches apart from traditional ML methods?
– Freelancing in ML: What does it take to succeed as a freelancer in the machine learning space?
– Future ML Skills: Which competencies will be critical for ML engineers in the AI-driven future?
This conversation aims to bridge the gap between academic knowledge and industry application, offering actionable insights on implementing machine learning solutions.
This fireside chat is relevant for students, practitioners, and leaders in the ML space, providing actionable insights and a realistic perspective on the current and future state of machine learning engineering.
00:00 Introduction to Santiago
00:48 Machine Learning Lifecycle Overview
07:27 Importance of Labeling and Monitoring
09:42 Practical Examples of Model Drift and Monitoring
12:18 Deploying Models to Production: Challenges and Solutions
16:30 Relationship Between Traditional ML and Generative AI
24:18 Usefulness and Limitations of LLMs
29:08 Future Directions and Research in AI
31:14 Skill Set Evolution and the Role of LLMs in Coding
36:35 Importance of Coding and System Design
42:26 Navigating the AI Hype Cycle
45:07 Freelancing in Machine Learning
56:00 Advice for Aspiring Machine Learning Professionals
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