90d roadmap · 3 milestones
12-Week AI Models & Training
Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Three four-week milestones move from mechanisms to production trade-offs and a measured synthesis artifact.
Outcome: Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation.
How to work this roadmap
Treat each milestone as a claim that must be supported by evidence. Before reading, write what you think the mechanism does and where it will fail. After studying the linked concepts, run the drills without copying an answer, preserve the output, and revise the explanation. Move forward when you can connect the milestone goal to a working implementation, benchmark, architecture decision, or reviewable design artifact.
The 90d horizon is a sequencing aid, not a completion badge. Spend more time where your prediction and the observed behavior disagree. Keep a short decision log containing the mechanism selected, alternatives rejected, expected failure mode, measurement used, and remaining uncertainty. Review that log with FSRS prompts so the roadmap produces durable system judgment rather than a temporary tour of terminology.
At the end, explain Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. from first principles to a reader outside the domain. A strong explanation should survive follow-up questions about correctness, cost, latency, resource use, security, recovery, and operational visibility. If it cannot, return to the milestone that contains the missing mechanism and build a smaller falsifiable example.
Milestones
Milestone 1
Weeks 1-4 — Foundations and mechanisms
Build the domain vocabulary and explain the core mechanisms from first principles.
Concepts
Milestone 2
Weeks 5-8 — Production systems and trade-offs
Design the production path, including resource, scale, safety, and operability trade-offs.
Concepts
Milestone 3
Weeks 9-12 — Reliability, verification, and synthesis
Test failure modes, measure outcomes, and ship the synthesis artifact.
Concepts
- LoRA & PEFT
- RL Alignment (GRPO & Policy Gradient)
- Training Data Engineering
- Model Quantization
- Open-Weight Models
- Multimodal Models
- Vision Models
- Voice & Audio Systems
- Model Evaluation
Build evidence
- Synthesize: AI Models & Training — Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.