Uploaded February 2026 | Updated September 2026, 1 week ago
Meet Sairam, 14+ years of hands on AI experience across Qualcomm, Intel Labs, Valeo, and NASAâs Frontier Development Lab. From research published at NeurIPS to real-world AI systems used at scale, his work focuses on one thing: turning complex AI into practical, production-ready systems.
In this session, weâll cover:
1. How do you tell if someone truly understands ML versus just knowing how to run models?
2. Whatâs one thing people focus too much on, and one thing they donât focus enough on when breaking into ML or GenAI?
3. With AI coding assistants everywhere, what skills matter more now? Whatâs becoming non-negotiable for engineers?
4. When shipping a GenAI feature, whatâs your personal production checklist?
Teams struggle with evaluation. How do you measure real business impact, not just model performance?
5. When ML systems scale, what usually breaks first? What was your first real scaling lesson?
6. For agent-based systems, how do you prevent silent failures or runaway behavior?
7. What are hiring managers actually looking for in ML and GenAI roles today?
8. If a software or data engineer wants to move into ML systems, whatâs the smartest path without falling into common traps?
This is a live, interactive session, bring your questions. If youâre building AI systems or planning a career in ML, this conversation will focus on what actually works in the real world.
We will also giveaway 50% off voucher code for Sairam's popular Live Training session, "Machine Learning and Generative AI System Design Workshop" to 2 lucky winners during the session. Comment with #PACKT to enter the raffle
Meet Sairam, 14+ years of hands on AI experience across Qualcomm, Intel Labs, Valeo, and NASAâs Frontier Development Lab. From research published at NeurIPS to real-world AI systems used at scale, his work focuses on one thing: turning complex AI into practical, production-ready systems.
In this session, weâll cover:
1. How do you tell if someone truly understands ML versus just knowing how to run models?
2. Whatâs one thing people focus too much on, and one thing they donât focus enough on when breaking into ML or GenAI?
3. With AI coding assistants everywhere, what skills matter more now? Whatâs becoming non-negotiable for engineers?
4. When shipping a GenAI feature, whatâs your personal production checklist?
Teams struggle with evaluation. How do you measure real business impact, not just model performance?
5. When ML systems scale, what usually breaks first? What was your first real scaling lesson?
6. For agent-based systems, how do you prevent silent failures or runaway behavior?
7. What are hiring managers actually looking for in ML and GenAI roles today?
8. If a software or data engineer wants to move into ML systems, whatâs the smartest path without falling into common traps?
This is a live, interactive session, bring your questions. If youâre building AI systems or planning a career in ML, this conversation will focus on what actually works in the real world.
We will also giveaway 50% off voucher code for Sairam's popular Live Training session, "Machine Learning and Generative AI System Design Workshop" to 2 lucky winners during the session. Comment with #PACKT to enter the raffle
