How to become an AI/ML Architect in 2025 | From PhD To Machine Learning Expert

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Cloud Career Mentor

71 episodes

4 days ago

Developing the next generation of cloud professionals

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Developing the next generation of cloud professionals

Technology

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How to become an AI/ML Architect in 2025 | From PhD To Machine Learning Expert

Cloud Career Mentor

33 minutes 48 seconds

9 months ago

How to become an AI/ML Architect in 2025 | From PhD To Machine Learning Expert

In this conversation, Rola Dali, a machine learning architect, shares her journey from academia to the tech industry. She discusses the challenges of leaving academia, the transition to a startup environment, and the critical role of cloud services in machine learning. Rola highlights the importance of problem-solving skills, a strong technical foundation, and the realities of working in AI engineering. She also explores the challenges of implementing machine learning solutions, the machine learning lifecycle, and the significance of intelligent document design in the shift to digital systems. Additionally, she talks about community engagement, including initiatives like the AWS Montreal user group and free GenAI workshops. Connect with Cloud Career Mentor 🗞️ Get a FREE guide on 3 Simple Steps To First Cloud Job: https://app.cloudcareermentor.com/podcast-3-simple-steps-opt-in 💻 Join Cloud Career Acceleration Program: www.cloudcareermentor.com 🎙️ Stream our podcast: podcasters.spotify.com/pod/show/cloudcareermentor Connect with me on social media 📲 LinkedIn: https://www.linkedin.com/in/fayomi-f/ 🐦 Twitter: twitter.com/thecloudmentor Connect with Rola Dali 📲 ⁠LinkedIn⁠: https://www.linkedin.com/in/roladali/ Chapters 00:00 - Introduction to AI Engineering and Machine Learning 01:52 - Rola's Academic Journey and Transition to Industry 06:06 - Navigating Guilt and Challenges of Leaving Academia 08:54 - First Steps in the Tech Industry 11:55 - Transitioning to Software Development 15:01 - The Role of Cloud Services in Machine Learning 17:54 - Real-World Applications and Projects in AI 20:14 - Challenges in Machine Learning Implementation 21:58 - The Reality of AI Engineering Careers 26:28 - The Machine Learning Lifecycle 30:52 - Attributes for Success in Tech Careers 32:43 - Community Engagement and Learning Opportunities