Core ML
Learn features, objectives, generalization, and why a metric is only meaningful beside the data that produced it.
Inspect: data -> objective -> errorEngineer the AI beneath the interface.
Follow one request through the stack, connect four foundations, and leave with a hands-on plan shaped for your engineering role.
Built for software engineers
Four foundations. One working system.
AI intuition arrives when concepts stop living in separate chapters. The model changes the distribution. The serving layer changes the latency. The evaluation layer changes what “working” means.
LLM Systems
See tokens, attention, context, sampling, and retrieval as engineering constraints instead of model magic.
Inspect: prompt -> tokens -> distributionProduction AI
Design evaluations, fallbacks, feedback loops, and release gates around behavior that is probabilistic by default.
Measure: quality + latency + costML Infrastructure
Connect data lineage, accelerators, model registries, serving, caches, and observability into a dependable operating system.
Operate: version -> serve -> observeTrace one request, end to end.
Open each layer to inspect the artifact, engineering question, and failure mode it contributes. The interface is the smallest part of the system.
Think like a systems engineer.
Each lens changes the question you ask before shipping. Open a lens and translate familiar software instincts into AI engineering practice.
Build your four-week field plan.
Choose a role, confidence level, time budget, and focus areas. The lab prioritizes the sequence and creates a useful practice artifact.
Map the system. Build the intuition. Ship with evidence.
Your exported plan contains the role, priorities, weekly exercises, time budget, and completion checks needed to turn a roadmap into practice.
Create the plan