Foundational CS for AI Engineers
Computer architecture, operating systems, compilers, memory management, networking, algorithms, and systems programming - the CS fundamentals that make you a better ML engineer.
7 Modules. The CS Depth Most ML Engineers Skip.
Understanding what happens below Python is what separates senior engineers from the rest.
Computer Architecture
CPU pipeline, cache design, SIMD, NUMA, hardware performance counters, and ARM vs x86 for AI.
What you'll master
- CPU Pipeline and Instruction Execution
- Memory Hierarchy and Cache Design
- SIMD and Vectorization
- Multicore and NUMA Architecture
- Hardware Performance Counters
- Storage Hierarchy: SSD and NVMe
- ARM vs x86 for AI Workloads
- Hardware Acceleration Beyond GPU
8 lessons
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Operating Systems for ML
Processes, virtual memory, Linux scheduling, containers, signals, kernel bypass, and Linux tuning.
What you'll master
- Processes, Threads, and Coroutines
- Virtual Memory and Page Faults
- Linux Process Scheduling
- File Systems and IO Patterns
- Containers and Namespaces
- Signals and IPC for ML
- Kernel Bypass and DPDK
- Linux Performance Tuning
8 lessons
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Compilers and Runtimes
CPython internals, JIT with numba, torch.compile and XLA, LLVM/MLIR, Cython, and profiling.
What you'll master
- How Python Works Internally
- JIT Compilation and numba
- torch.compile and XLA
- LLVM and MLIR
- Cython and C Extensions
- Profiling Python and C Code
- Static Analysis and Type Systems
- Dependency Management and Packaging
8 lessons
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Memory Management
Heap and stack, GC algorithms, memory allocators, profiling, zero-copy transfers, and Rust.
What you'll master
- Heap and Stack Memory
- Garbage Collection Algorithms
- Memory Allocators for ML
- Memory Profiling and Debugging
- Zero-Copy and Data Transfer
- Memory Safety and Rust
- Large-Scale Memory Optimization
- Memory Models and Concurrency
8 lessons
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Networking for Distributed AI
TCP/IP, gRPC, Kafka, service mesh, network debugging for distributed training, and HTTP/3.
What you'll master
- TCP/IP Fundamentals for ML
- gRPC and Protocol Buffers
- Message Queues and Kafka
- Service Mesh and Load Balancing
- Network Debugging for Distributed Training
- HTTP/3 and QUIC
- DNS, Service Discovery, and Consul
- Network Security for ML Platforms
8 lessons
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Algorithms for ML
Complexity analysis, hash tables, dynamic programming, graph algorithms, and randomized algorithms.
What you'll master
- Complexity Analysis for ML Engineers
- Sorting and Search for ML
- Hash Tables and Bloom Filters
- Dynamic Programming for ML
- Graph Algorithms and GNNs
- Randomized Algorithms and Sketching
- Optimization Algorithms Deep Dive
- Data Structures for ML Systems
8 lessons
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Systems Programming
C/C++ for ML, concurrency primitives, Linux system calls, serialization, build systems, shell scripting, and IaC.
What you'll master
- C and C++ for ML Systems
- Concurrency Primitives
- System Calls and Linux API
- Serialization and Data Formats
- Build Systems and CI/CD for ML
- Shell Scripting for ML Workflows
- Observability and Logging
- Infrastructure as Code for ML
8 lessons
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Build on foundations that do not shift.
The engineers who understand the full stack are the ones who fix what everyone else cannot.
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