How long is the learning plan, and what does it teach?

The plan spans 10 weeks with 50 sessions at 30 minutes a day. It covers theory and hands-on building of an inference service, including the roofline model, vLLM and SGLang deployment, paged attention, observability dashboards, continuous batching, load testing at 1,000+ concurrent requests, quantization, speculative decoding, and KV cache eviction.

What is the Plan-and-Act pattern, and how does it differ from ReAct?

ReAct runs one model in a single loop, appending every thought, action, and observation to the same prompt; failed steps stay in context and compete for the model's attention. Plan-and-Act splits the task into two jobs: a planner writes high-level steps once, and an executor reads the plan and current state, takes one action, then strips unnecessary context before the next step. On WebArena-Lite, a properly trained planner with replanning after each action reached 53.94%, compared to 36.97% with no planner.

What are the phases of ML model development described in the newsletter?

Phase 1 is a non-ML baseline (rules or heuristics). Phase 2 introduces the simplest possible ML model (logistic regression, decision tree, k-nearest neighbors) to validate the end-to-end pipeline. Phase 3 extracts value through feature engineering, hyperparameter tuning, and more data—where returns on investment are often highest. Phase 4 moves to complex models like deep neural networks or transformers, only after simpler approaches are exhausted.

Large Language ModelsAI Coding AssistantsDaily Dose of Data ScienceAug 9, 2026Aug 10, 2026, 06:00 JST

10-week roadmap teaches engineers to deploy LLMs in production

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3 Key Points

What happened
A structured 10-week learning plan with 50 sessions teaches AI engineers how to build, deploy, and optimize inference services for large language models, covering topics from the roofline model and vLLM to load testing at 1,000+ concurrent requests and building a reproducible benchmark published on GitHub.
Why it matters
Most AI engineers lack a systematic path to production-grade inference work; this roadmap bridges theory and hands-on practice, showing how to measure performance (TTFT, inter-token latency, throughput, queue depth) and tune for cost and latency rather than jumping straight to deep learning or model selection, which often wastes effort.
What to watch
The roadmap also surfaces a broader principle—ML systems should evolve through phases starting with non-ML baselines (rules, heuristics) before adding simple models (logistic regression, decision trees), then tuning features and hyperparameters, and only then moving to complex deep learning—encouraging teams to validate each stage before scaling complexity.

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Context & Analysis

The newsletter addresses a critical gap in AI engineering: most engineers know how to train models, but far fewer know how to deploy them efficiently at scale. The 10-week roadmap tackles this by grounding the curriculum in observable, measurable outcomes—throughput, latency percentiles (p50, p95, p99), queue depth—rather than abstract best practices. It codifies the hard-won lessons of inference optimization: that decoding is memory-bound (not compute-bound), that prefix caching and continuous batching unlock real savings, and that speculative decoding has known limits.

The plan also embeds a second, quieter lesson about agent design and general ML systems. The comparison between ReAct and Plan-and-Act shows that a poorly designed harness—one that accumulates failed context—can hurt performance more than no planner at all (20.60% vs. 36.97%). This echoes the phased ML development framework: starting simple, validating incrementally, and resisting the urge to add complexity until earlier stages are exhausted. A well-tuned logistic regression often outperforms a hastily trained neural network, and a naively finetuned planner can drag down a competent executor. The lesson is consistent: engineering discipline and measurement beat raw model power.

FAQ

How long is the learning plan, and what does it teach?: The plan spans 10 weeks with 50 sessions at 30 minutes a day. It covers theory and hands-on building of an inference service, including the roofline model, vLLM and SGLang deployment, paged attention, observability dashboards, continuous batching, load testing at 1,000+ concurrent requests, quantization, speculative decoding, and KV cache eviction.
What is the Plan-and-Act pattern, and how does it differ from ReAct?: ReAct runs one model in a single loop, appending every thought, action, and observation to the same prompt; failed steps stay in context and compete for the model's attention. Plan-and-Act splits the task into two jobs: a planner writes high-level steps once, and an executor reads the plan and current state, takes one action, then strips unnecessary context before the next step. On WebArena-Lite, a properly trained planner with replanning after each action reached 53.94%, compared to 36.97% with no planner.
What are the phases of ML model development described in the newsletter?: Phase 1 is a non-ML baseline (rules or heuristics). Phase 2 introduces the simplest possible ML model (logistic regression, decision tree, k-nearest neighbors) to validate the end-to-end pipeline. Phase 3 extracts value through feature engineering, hyperparameter tuning, and more data—where returns on investment are often highest. Phase 4 moves to complex models like deep neural networks or transformers, only after simpler approaches are exhausted.

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