2 minute read

Welcome to The AI Engineering Weekly!

Hello and welcome to the inaugural issue of my newsletter! I’m thrilled to have you as a subscriber. As someone building scalable AI systems at AWS SageMaker AI, I want to share insights from the trenches of AI engineering with fellow practitioners and enthusiasts.

What This Newsletter Is About

This newsletter will be your regular dose of:

  • Technical insights from building AI systems at scale
  • Behind-the-scenes updates from my work on LLM fine-tuning and distributed training
  • Curated resources on ML infrastructure, optimization techniques, and emerging tools
  • Industry observations on the rapidly evolving AI landscape
  • Practical tips for engineers working on machine learning systems

Format and Frequency

I’m planning to send this newsletter bi-weekly (every two weeks), giving me time to curate quality technical content while keeping you updated on the latest developments. Each issue will be concise but packed with actionable insights - I respect your time and inbox.

This Week’s Highlights

📚 What I’m Reading

  • “Designing Data-Intensive Applications” by Martin Kleppmann - Still one of the best resources for understanding distributed systems principles that apply to ML infrastructure
  • “The Hundred-Page Machine Learning Book” by Andriy Burkov - A concise yet comprehensive ML reference I keep coming back to

🛠️ What I’m Working On

I’m currently diving deep into optimizing fine-tuning workflows for large language models. The challenge involves balancing compute efficiency, memory usage, and training speed while maintaining model quality. It’s fascinating how distributed training strategies need to adapt as model sizes continue to grow.

💡 Thought of the Week

“Premature optimization is the root of all evil” - Donald Knuth

This quote resonated with me this week as I worked on performance improvements. Sometimes the best optimization is understanding your bottlenecks first, rather than guessing where to focus your efforts.

🔗 Links Worth Sharing

A Personal Note

Starting this newsletter feels like the beginning of an important conversation with the AI engineering community. The field is moving so quickly, and I believe we all benefit from sharing our experiences building these systems in production.

If you have any feedback, questions about specific AI engineering topics, or want to share your own experiences, feel free to reply to this email. I read every response and love connecting with fellow engineers.

What’s Coming Next

In the next issue, I’ll be sharing:

  • Lessons learned from optimizing LLM inference at scale
  • A comparison of different fine-tuning strategies and when to use each
  • Deep dive into cost optimization for ML workloads on cloud platforms

Thank you for subscribing! If you found this valuable, please consider sharing it with other AI engineers who might benefit from these insights.

Until next time, Shiva Maruth Alapati

P.S. You can always catch up on previous issues in the newsletter archive on my website.

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