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12-week elite track
Build the distributed, real-time substrate AI runs on.
Engineer the systems frontier AI depends on: large-scale distributed training, real-time and streaming AI, GPU cluster scheduling, vector databases at scale, and the consistency and fault-tolerance tradeoffs underneath it all. You leave able to reason about and operate planet-scale AI infrastructure. Bridges to Distributed Systems, Databases, and Operating Systems.
Week by week
Every week unlocks the next. Concepts route you to free, world-class material; projects turn that knowledge into something deployed.
Week 1
Latency, partial failure, and the eight fallacies. Why a single-process mental model breaks the moment AI infrastructure spans more than one machine.
Bridges to Distributed Systems — failure models, latency, and the CAP theoremBuilds on: nothing — start here
Week 3
How distributed components agree under failure: Raft, leader election, replicated logs, and the coordination primitives that GPU schedulers and metadata stores depend on.
Bridges to Distributed Systems — consensus, replication, and fault toleranceBuilds on: Distributed Systems Foundations
Week 4
Train a model too big for one GPU: data, tensor, pipeline, and fully-sharded parallelism, plus the collective communication (all-reduce) that makes them work.
Bridges to Parallel Computing — collective communication and parallel decompositionBuilds on: Consensus & Coordination
Week 6
Pack expensive accelerators efficiently: gang scheduling, fairness, preemption, topology-aware placement, and the scheduling tradeoffs that decide cluster utilization.
Bridges to Operating Systems — scheduling, resource allocation, and fairnessBuilds on: Distributed Training & Parallelism
Week 7
Process events as they arrive: streaming logs, exactly-once semantics, windowing, backpressure, and serving low-latency inference on a live data stream.
Bridges to Distributed Systems — event-driven architecture and stream processingBuilds on: GPU Cluster Scheduling
Week 8
An enterprise mandate: train a model that does not fit on one GPU, and turn the result into a launched product. Build a distributed training setup that uses data and at least one model-parallel strategy (tensor, pipeline, or fully-sharded), with correct collective communication, checkpointing that survives a node failure, and a throughput report. The run will be long; it must resume cleanly from a checkpoint after a simulated crash. The deliverable is not just training logs — the resulting model must be served as a directly deployable, hyperscalable product: a real public API with a hyper-usable demo UI, autoscaling, CI/CD, observability, security, and a live training-metrics dashboard. It ships complete with marketing — a landing page, a pitch, and a demo. We are not here to babysit a job that loses days of compute on one failure; ship the model as a real product.
Why it matters: Distributed training is the backbone skill behind every frontier model, and few engineers can debug a stalled all-reduce or a corrupt checkpoint. A builder who ships a fault-tolerant multi-GPU training system is directly deployable as a Distributed Systems or Training Infrastructure Engineer, one of the scarcest and highest-paid frontier roles, well into ₹1-crore-tier compensation.
A public repo, a benchmark report, and a publicly hosted product with a stable URL and a hyper-usable demo UI serving the trained model: the distributed training configuration, the parallelism strategy, the checkpoint-and-resume logic, a live training-metrics dashboard, an autoscaling serving deployment, CI/CD on every commit, production observability, a scaling report across GPU counts, a marketing landing page, a 10-slide pitch, a recorded demo, and a README documenting the communication pattern, the failure-recovery design, and the serving-scale design.
Market signal — who wants thisDistributed training infrastructure is a heavily funded 2026 category: Gartner projects $37.5B of end-user spending on AI-optimized infrastructure in 2026, and a market of training-orchestration products has formed — CoreWeave's Kubernetes-native GPU cloud, dstack's distributed-training orchestration, NVIDIA's open-source KAI Scheduler with gang scheduling, and NVIDIA Run:ai. Investors fund training infrastructure that keeps expensive clusters above 70% utilization; the scarce, decisive skill is making multi-GPU jobs fault-tolerant and efficient, not merely launching them.
Week 9
Index and serve billions of embeddings: HNSW and IVF indexes, sharding, quantization for storage, and the recall-latency-cost surface of large-scale similarity search.
Bridges to Databases — indexing, sharding, and query optimizationBuilds on: Real-Time & Streaming AI Systems
Week 11
Build systems that survive failure: checkpointing long training runs, circuit breakers, retries with backoff and jitter, graceful degradation, and SLO-driven operations.
Bridges to Distributed Systems — fault tolerance, checkpointing, and recoveryBuilds on: Vector Databases at Scale
Week 12
Operate a fleet you can see: metrics, traces, and logs across nodes; SLOs and error budgets; and capacity planning so an AI cluster neither starves nor burns money idle.
Bridges to Distributed Systems — monitoring, capacity planning, and performance analysisBuilds on: Fault Tolerance & Resilient Operations
An enterprise mandate: build and launch a platform that consumes a high-rate live event stream, runs inference on each event with low latency, indexes results into a vector store, and serves real-time similarity queries — all while staying healthy under bursty load and a node failure. This is a distributed-systems problem with AI inside it: exactly-once or well-reasoned delivery semantics, backpressure, autoscaling, GPU-aware scheduling, and observability across the fleet. The deliverable is a directly deployable, hyperscalable product: real public hosting, CI/CD, security, a hyper-usable real-time dashboard and query UI, and full marketing — a landing page, a pitch, and a demo. Deliver a platform that does not fall over when traffic spikes, that a buyer can evaluate, and that is presentable as a real product. Ship it as a real product.
Why it matters: Real-time AI on live data powers fraud detection, recommendations, and observability products across every major platform. A builder who ships a streaming inference platform that holds up under load and failure is directly deployable as a senior Distributed Systems or Real-Time AI Engineer, a ₹1-crore-tier role because it demands both systems depth and AI fluency.
A publicly hosted platform with a stable URL and a hyper-usable real-time dashboard plus query UI, plus a public repo: the streaming ingestion and inference pipeline, the at-scale vector index, the autoscaling and backpressure design, CI/CD on every commit, fleet observability, a load-and-failure test report, a marketing landing page, a 10-slide pitch, a recorded demo, and a README documenting delivery semantics, fault tolerance, security, and capacity planning.
Market signal — who wants thisReal-time streaming AI is a funded 2026 category anchored in fraud detection and live personalization: Artie raised a $12M Series A to make real-time data the default for AI systems, Experian launched real-time AI fraud detection with Resistant AI's 80+ models, and global fintech venture funding hit $12B across 751 deals by April 2026. Production fraud models need sub-millisecond feature retrieval and 20-100+ features within a 100ms window, served by vector databases like Pinecone, Milvus, and Redis. Investors fund streaming-AI platforms because regulated finance and large consumer platforms must score live events instantly or lose money.