AI Engineering: From Theory to Scalable Systems
Implementing the infrastructure behind modern AI (What research papers leave out)
There’s a gap between understanding AI systems and building them. You can read papers explaining why attention mechanisms work, follow tutorials on distributed training concepts, or study the math behind gradient descent. But when the task is to implement a production training pipeline that scales to 70 billion parameters (or more, lol) across a dizzying number of GPUs, the real challenges live in the details the textbooks don’t cover.
A data-parallel job runs at 60% efficiency despite fully utilized GPUs because of a microsecond-level timing mismatch in gradient synchronization. A pipeline parallel schedule deadlocks unpredictably, freezing dozens of GPUs when one stage finishes a step slightly ahead of the rest. (That one was especially gnarly.) Off-the-shelf attention implementations blow past GPU memory, forcing a rethink of memory layout. Serving 100,000 requests per second is easy on paper, but naive batching kills latency. And RLHF PPO training loops? They’ll explode without careful reward normalization and clipping across distributed learners.
These are the kinds of challenges that separate reference papers, conceptual explanations, and scattered “tips & tricks” posts from implementations that actually run at scale. This is the gap Build AI exists to close. The series takes a build-first approach, showing how to implement AI system infrastructure, memory architectures, and end-to-end inference pipelines that turn theory into real-world systems. Every implementation is designed to run, profile, and teach the systems intuition that only experience provides, based on the fundamental principle that the best way to understand such systems is to literally build the working implementation.
What Makes This Different
Most AI resources fall into two camps. Academic papers provide deep theoretical insights but leave implementation as “future work.” Framework documentation shows you how to use APIs but hides the underlying algorithms. Most dedicated books offer a kind of conceptual middle ground where you get a little of each, but not enough to build or maintain anything serious. And none of those prepare engineers for the subtleties of production-scale systems needed to be a valued team-member, such as overcoming deadlocks, memory bottlenecks, inefficient communication patterns, or pipeline inefficiencies.
Build AI aims to provide the missing layer: the bridge between theory and working systems. The focus is on implementing the infrastructure behind modern AI, turning theory into production-ready code. Every example is constructed to teach through experimentation: you’ll write the algorithms, see where they fail, profile performance, and apply optimizations until they scale. This is where theoretic understanding becomes practical knowledge.
The Five-Part Journey
BUILD AI combines core algorithm implementation with debugging, optimization, and systems thinking, divided into 5 core modules:
Module I: Foundation Model Training Infrastructure
Implement tensor, pipeline, and data parallelism from first principles. Explore memory optimizations like FlashAttention and gradient checkpointing, and build data pipelines that keep GPUs fully utilized without bottlenecks.
Module II: Training Large Models
Implement transformers and full training loops. Optimize attention mechanisms, learning rate schedules, and evaluation frameworks across multiple devices.
Module III: Fine-tuning and Alignment
Build parameter-efficient fine-tuning pipelines like LoRA and QLoRA. Implement RLHF PPO adapted for language models, instruction tuning, and constitutional AI training loops.
Module IV: Deployment and Serving
Optimize inference for production. Implement continuous batching, speculative decoding, auto-scaling, and model parallelism for serving. Explore quantization techniques for edge deployment.
Module V: Advanced Topics
Build custom training infrastructure, experiment tracking, hyperparameter optimization systems, and multimodal extensions like vision-language models and cross-modal attention.
Who This Is For
This series is for team members and engineers building AI infrastructure, implementing research in production, and scaling systems beyond the prototype phase. (By the end of the current sprint!) It’s also for anyone who wants to understand the algorithms behind frameworks, not just working with the APIs they expose.
You’ll tackle distributed training bottlenecks, design serving optimizations, and create training platforms that other teams can rely on. Build AI provides the layer between abstract knowledge and hands-on production experience, giving you the systems intuition —and hands-on experience— necessary to execute at scale.
Getting Started
To run the code examples and implement a full AI pipeline at scale, you'll need access to a multi-GPU cluster. The easiest way to get one is through a managed cloud service like AWS, Google Cloud, or Oracle Cloud. These platforms offer pre-configured GPU clusters that allow you to start running workloads immediately, so you don't have to deal with complicated networking or storage setup. (Which we have other teams for.)
These clusters come with full multi-node capability right out of the box, allowing you to focus on building and scaling AI pipelines, not on managing infrastructure. Each provider offers detailed documentation to help you configure jobs, monitor GPU utilization, and scale efficiently. This ensures you can get the examples in this series running on real hardware from day one.
Cloud Provider Pricing
The pricing for these powerful clusters varies based on how the cloud provider bills for their resources. Each platform offers different pricing models (e.g., on-demand, reserved, or spot instances) and costs can vary significantly by region and GPU type. All prices as of time of writing.
On AWS, a p3.16xlarge instance with 8 V100 GPUs (billed as one unit) currently costs $24.48 per hour on-demand. (US pricing. ~$35.00 in a more expensive region such as Europe/Asia-Pacific.)
Google Cloud: Charges a per-GPU rate for its instances. An NVIDIA V100 GPU has an current on-demand cost of $2.48 per hour, which would make a cluster with 8 GPUs cost $19.84 per hour.
Oracle Cloud: Also uses a per-GPU rate. Its A100 GPUs are priced at $2.40 per hour, so a cluster with 8 GPUs would cost $19.20 per hour.
LFG (Let’s Fearlessly Go!)
Every implementation in the BUILD AI series is designed to teach through practice: write the algorithms, profile performance, identify bottlenecks, and optimize until they meet product requirements. It’s where understanding meets doing, and my goal is to give you the intuitions that separates engineers who build and operate AI infrastructure at scale from those who mainly use it as deployed.
The gap between understanding AI systems and building them is where most practical engineering happens. Build AI exists to close that gap. One working implementation at a time.
Next: We jump right into module one, Distributed Training from Scratch. Or, take a look at the full BUILD AI series outline (potentially subject to change as we go along.)
Cheers!
Forest Mars