Pikachu

Software Engineer

Yiran(Vivian) Xu

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

ex-Founder and Research Engineering Lead @ Abide AI 

AI Inference Engineering & Systems Design

Computer Scientist and ML Engineer

<h3>Why I enrolled</h3><p class="p2-tall">As a Backend Software Engineer, I recognize that the scarcity and efficiency of computational resources remain the primary bottlenecks for scaling AI. I joined this program to bridge the gap between traditional backend infrastructure and the specialized world of AI Inference. My goal was to master the fundamentals of building high-performance, end-to-end inference systems that can thrive under real-world resource constraints.</p><p class="p2-tall"></p><p class="p2-tall"></p><h3>What I’ll apply</h3><p class="p2-tall">I plan to implement agentic systems to enhance observability within complex data pipelines. By integrating these intelligent agents, I aim to move from standard monitoring to a more proactive, automated system for tracking health and performance in high-throughput environments.</p><p class="p2-tall"></p>

Certificate

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