Mewtwo

Agentic Engineer

Colin Duggan

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building, training, and deploying machine learning models — from classical ML to deep learning.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Most engineers load models with Hugging Face then serve something basic and get stuck on cost, latency, or scale. In this course, we will 

Computer Scientist, ML Engineer & Author

O'Reilly Media

@Packtpub

Abi Aryan

AI Infrastructure Engineer, Stealth

AI Inference Engineering & Systems Design

Computer Scientist and ML Engineer

<h3>Why I enrolled</h3>

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<h3>What I’ll apply</h3>
<p class="p2-tall">I'm most excited to size open-weight models for the agents I build. I'll work out the memory, KV headroom and throughput up front, so I'm not guessing which GPU to rent. I also want to bring the gateway patterns into my platform work: routing that knows where the cache is, admission and queueing by priority, and scaling on KV demand. Then self-hosted models can serve agentic workloads reliably and keep sensitive data in-house.</p>
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Certificate

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