Schedule on Learning Notes

First

Mon, 01 Jan 0001 00:00:00 +0000

From Kubernetes/SRE to AI SRE and MLOps #

Goal and Starting Point #

Build on existing Kubernetes and SRE knowledge to move toward AI infrastructure / inference SRE, while developing a practical Machine Learning Operations (MLOps) foundation.

The recommended progression is:

Kubernetes/SRE → ML lifecycle fundamentals → AI serving and reliability → deeper MLOps specialization.

Technical baseline: Existing Kubernetes and SRE knowledge. Python and hands-on ML proficiency still need to be assessed.
English baseline: Self-reported B2. Practice explaining engineering decisions, investigating incidents, and discussing trade-offs in English.
Planning estimate: A 16-week first iteration at approximately 10–12 hours per week, assuming basic programming ability. Add preparation time if Python is unfamiliar.
Progress rule: Advance when the practical completion criteria are met, not simply when a week ends. This schedule is not a guarantee of job readiness.
Learning model: Use the existing English-learning plans as supporting resources, not as substitutes for implementation, testing, and troubleshooting.

1. Choose a Primary Direction #

Role titles vary across companies. Evaluate job descriptions by their responsibilities rather than by the title alone.

AI Topic

Mon, 01 Jan 0001 00:00:00 +0000

How to Use This Plan #

Tracking progress: The first column is blank. Fill it in when you finish a day so you can see completed and pending days at a glance.
Schedule: 12 weeks, 6 study days per week, about 30 minutes per day. Day 01–72 are study days, not calendar dates. Use the seventh day of each week for rest or catching up.
Goal: Explain AI concepts, summarize information, and discuss trade-offs in English at a B2 target level. These resources do not have official CEFR ratings.
Daily routine: 3 minutes of recall, 10 minutes of video study, 8 minutes of reading, and 9 minutes of speaking or writing. Save three useful phrases, not a long list of isolated words.
Manage the workload: Study a 3–6-minute video segment and roughly 300–500 words of the article. There is no need to finish a long video or article in one sitting. Focus labels below describe what to look for, not exact chapter titles or timestamps.
Repeat deliberately: Each week reuses one video/article pair. Listen for the main idea, check details with English captions when available, imitate a short passage, explain the idea, then review without notes.
Adapt the difficulty: If a segment is too difficult, shorten it to 1–2 minutes and use captions or a slower playback speed. Weeks 4 and 11 may require this extra support.
Sources: Videos are from IBM Technology/IBM Think, except Week 4 from 3Blue1Brown. Articles are from IBM Think. They are useful language material but reflect a vendor perspective; separate educational explanations from product promotion. Video links lead to individual YouTube videos or IBM pages with embedded videos; playback and caption availability depend on your region and browser.

Week 1 — AI, Machine Learning, and Deep Learning #

Video: AI, Machine Learning, Deep Learning and Generative AI Explained + Machine Learning vs Deep Learning + AI vs Machine Learning
Article: What is artificial intelligence (AI)?

MLOps

Mon, 01 Jan 0001 00:00:00 +0000

Week 1 — MLOps Foundations: Lifecycle, Data, Experiments, and Registry #

Day	Topic	Video	Article	English Practice
01	What is MLOps and why does it matter?	MLOps Explained in 10 Minutes \| Complete Beginner Guide — listen for the definition and key practices	What is MLOps? — Machine Learning Operations Explained — definition and scope	Record a 60-second baseline explanation of MLOps without a script.
02	How does the ML lifecycle differ from traditional software?	What Is MLOps? Machine Learning Lifecycle Explained — follow the lifecycle stages	MLOps: Continuous Delivery and Automation Pipelines in Machine Learning — lifecycle and maturity levels	Describe the ML lifecycle in four ordered sentences using “training,” “validation,” “deployment,” and “monitoring.”
03	Why does data versioning matter in ML?	Versioning Data with DVC (Hands-On Tutorial!) — follow the DVC workflow	Versioning Data and Models — DVC Documentation — how data versioning works	Explain why Git alone is not enough for ML data using “whereas,” “large-scale,” and “reproducibility.”
04	What is experiment tracking and why do we need it?	Introduction To MLflow — An Open Source Platform for the Machine Learning Lifecycle — follow the experiment tracking workflow	MLOps: Why Data and Model Experiment Tracking Is Important — importance of tracking	Write five sentences about experiment tracking using “parameters,” “metrics,” “reproducibility,” and “in order to.”
05	What role does a model registry play?	ML Model Registry Part 1: Logging and Registering in Weights & Biases — follow the registration workflow	Exploring the Role of an ML Model Registry — model registry fundamentals	Describe what a model registry stores using “versioning,” “metadata,” “lineage,” and “staging.”
06	Review: summarize the MLOps landscape	Machine Learning Engineering for Production (MLOps) — replay the course overview without captions	MLOps Principles — verify your understanding of the MLOps landscape	Give a two-minute explanation of the MLOps landscape; compare your clarity with Day 01.

Week 2 — Reproducibility and Training Pipelines #

Day	Topic	Video	Article	English Practice
07	What is a feature store and why do we need one?	Feature Stores: What They Are & When You Really Need One — listen for the definition and use cases	What Is a Feature Store? — definition and benefits	Explain what a feature store does in three sentences using “centralized,” “reusable,” and “in order to.”
08	How do you build a reproducible ML pipeline?	Run your first MLOps pipeline in 11 minutes — follow the pipeline setup and deployment	What Is a Machine Learning Pipeline? — pipeline stages and benefits	Describe the pipeline stages in four ordered sentences using “ingest,” “validate,” “train,” and “deploy.”
09	What is hyperparameter tuning and how does it work?	Tuning and scaling your ML models — follow the tuning strategies	What Is Hyperparameter Tuning? — tuning methods and best practices	Explain grid search and random search using “whereas” and “in order to.”
10	How do you evaluate a model before deployment?	Model Evaluation in MLOps \| Cross-Validation Before Deployment — follow the evaluation workflow	What is Model Evaluation? — evaluation metrics and techniques	Write five sentences about model evaluation using “cross-validation,” “baseline,” “metric,” and “however.”
11	How do you deploy a model to production?	How to Deploy ML Models to Public Cloud in 2026 \| Production Guide — follow the deployment steps	What Is Model Deployment? — deployment methods and considerations	Describe the deployment path in four ordered sentences using “package,” “containerize,” “serve,” and “monitor.”
12	Review: explain the path from experiment to production	What is MLOps? — replay the full MLOps overview without captions	What is MLOps? — verify your understanding of the MLOps pipeline	Give a two-minute explanation of the path from experiment to production; compare your clarity with Day 07.

Week 3 — Deployment and Monitoring #

Day	Topic	Video	Article	English Practice
13	What is model serving and how does it work?	How to Deploy Machine Learning Models (ft. Runway) — follow the serving options	What is an AI Stack? — model deployment and serving layers	Explain model serving in three sentences using “real-time,” “batch,” and “endpoint.”
14	What is CI/CD for machine learning?	Introduction to CI/CD \| Deployment of Machine Learning Models — follow the pipeline steps	What Is CI/CD? — CI/CD pipeline and benefits	Describe a CI/CD pipeline in four ordered sentences using “build,” “test,” “stage,” and “deploy.”
15	What is data drift and how do you detect it?	What is Concept and Data Drift? — listen for the definition and examples	What Is Model Drift? — drift types and detection methods	Explain data drift using “distribution,” “shift,” and “monitoring” in three sentences.
16	What is model drift and how do you handle it?	ML Drift: Identifying Issues Before You Have a Problem — follow the drift detection workflow	What is concept drift in ML, and how to detect and address it — concept drift causes and responses	Compare data drift and concept drift using “whereas” twice.
17	How do you monitor ML models in production?	Monitoring ML Models & Data in Production — follow the monitoring metrics	Guide to Monitoring Machine Learning — monitoring pillars and observability	Write five sentences about ML monitoring using “metrics,” “alerts,” “feedback loop,” and “in order to.”
18	Review: explain the production ML monitoring setup	Detect & Mitigate Data Drift in Production Machine Learning Systems — replay the drift detection recap without captions	What Is the AI Lifecycle? — verify your understanding of the AI lifecycle	Give a two-minute explanation of production ML monitoring; compare your clarity with Day 13.

Week 4 — MLOps at Scale: Orchestration, Maturity, and Trade-offs #

Day	Topic	Video	Article	English Practice
19	What is pipeline orchestration in MLOps?	Kubeflow vs MLFlow — listen for the orchestration comparison	What is Workflow Orchestration? — orchestration patterns and tools	Describe an orchestration workflow in four ordered sentences using “trigger,” “schedule,” “task,” and “in order to.”
20	What does an end-to-end MLOps platform look like?	End-to-End MLOps in Azure \| Azure Machine Learning — follow the architecture overview	What is an AI Platform? — platform capabilities and components	Write five sentences about an MLOps platform using “integrated,” “end-to-end,” “governance,” and “however.”
21	What are the MLOps maturity levels?	MLOps Maturity Level 0 to 1 — follow the maturity progression	MLOps Maturity Model — levels 0 through 3 explained	Compare levels 0 and 2 using “whereas” twice; then summarize level 3 in two sentences.
22	What is ML technical debt and how do you manage it?	What is AI Technical Debt? — listen for the hidden costs of ML systems	What is Technical Debt? — debt types and remediation	Describe two sources of ML technical debt using “data dependencies,” “model decay,” and “in order to.”
23	How do you decide between building or buying an MLOps platform?	From Scratch to Success: Building an MLOps Team and ML Platform — follow the platform decision framework	MLOps Platform: Build vs. Buy? What You Must Know — pros and cons of each approach	Compare build vs buy using “whereas” and “on the other hand”; recommend one approach in two sentences.
24	Review: explain the MLOps journey	MLOps Explained - What It Is, Why You Need It and How It Works — replay the full MLOps overview without captions	What is AI Infrastructure? — verify your understanding of MLOps at scale	Give a three-minute presentation connecting Weeks 1–4; compare your clarity with Day 01.

Weekly Self-Check #

After each sixth study day, record:

SRE

Mon, 01 Jan 0001 00:00:00 +0000

Week 1 — SRE Foundations: Reliability Measurement and Alerting #

Day	Topic	Video	Article	English Practice
01	What are SLIs, SLOs, and error budgets?	SLO vs SLI vs SLA vs Error Budget \| Google SRE in Plain English — listen for the definitions	SRE Fundamentals: Differences Between SLI vs. SLO vs. SLA — core concepts	Define each term in one sentence using “measures,” “targets,” and “tolerates.”
02	How do you measure what matters?	Understanding SLIs, SLOs, SLAs & Error Budgets — follow the measurement framework	SLOs: Stop Thinking in Burn Rates — how to track reliability	Explain burn rate in three sentences using “error budget” and “window.”
03	What makes a good alert vs. noise?	Why Alert Fatigue is a Major Challenge in Observability — focus on the survey findings	How AI Can Help IT Teams Find the Signals in Alert Noise — reducing noise	Write five rules for actionable alerts using “must” and “in order to.”
04	How does toil erode operational reliability?	Reducing Toil \| Site Reliability Engineering (SRE) Foundation — follow the toil characteristics	5 Non-AI Reasons Why You Still Have Toil — why toil persists	Describe two examples of toil using “repetitive,” “automatable,” and “no long-term value.”
05	What is the relationship between SRE and DevOps?	SRE vs DevOps vs Platform Engineering — The Honest Difference — follow the role boundaries	5 Ways to Build out an SRE Function and Why It Matters — building an SRE team	Compare SRE and DevOps using “whereas” and “on the other hand.”
06	Review: define reliability targets for a service	Introduction to Site Reliability Engineering — replay without captions	What’s the Difference Between Observability and Monitoring? — verify your understanding	Give a two-minute explanation of SRE foundations; compare with Day 01.

Week 2 — Incident Response, Postmortem, and Chaos Engineering #

Day	Topic	Video	Article	English Practice
07	How should an incident be detected and triaged?	Navigating SRE/Incident Management — follow the triage workflow	How We Manage Incident Response at Honeycomb — real-world process	Describe the triage steps in four ordered sentences using “detect,” “classify,” and “escalate.”
08	What happens during an incident war room?	Automated Cloud-Native Incident Response with Kubernetes and Service Mesh — watch how the bridge is structured	Tips to Make Your On-Call Process Less Stressful — war room roles and communication	Write five rules for an effective war room using “must,” “in order to,” and “without delay.”
09	How do you write a blameless postmortem?	Postmortem Culture at Google \| Ramon Medrano Llamas — focus on the blameless principles	Top 12 Best Practices for Better Incident Management Postmortems — postmortem checklist	Summarize a postmortem structure in five sentences using “timeline,” “root cause,” and “action item.”
10	What is chaos engineering and why practice failure?	Deep Dive into Chaos Mesh — follow the chaos experiment lifecycle	There Is No Resilience without Chaos — chaos engineering philosophy	Explain chaos engineering in three sentences using “hypothesis,” “blast radius,” and “steady state.”
11	How do you build resilience with redundancy and fallbacks?	Kubernetes Workload Resiliency in Action: Beyond Basics — focus on the resiliency patterns	Chaos Engineering on CI/CD Pipelines — testing resilience in pipelines	Describe two resilience patterns using “circuit breaker,” “retry,” and “fail gracefully.”
12	Review: run a tabletop incident exercise	Games We Play to Improve on Incident Response \| Conf42 SRE 2021 — replay the exercise formats	Bringing Principles into the World of Incident Management — verify your exercise design	Give a two-minute walkthrough of a tabletop exercise; compare with Day 07.

Week 3 — Autoscaling, Performance, and Capacity Planning #

Day	Topic	Video	Article	English Practice
13	How does horizontal pod autoscaling work?	Horizontal Pod Autoscaling \| Kubernetes — follow the HPA scaling loop	Getting Started with Kubernetes Autoscaling — HPA fundamentals	Explain how HPA decides when to scale in four sentences using “threshold,” “metrics,” and “replicas.”
14	What is the difference between HPA, VPA, and KEDA?	HPA vs. VPA vs. Keda vs. CA vs. Karpenter vs. Fargate — follow the comparison matrix	K8s Resource Management: An Autoscaling Cheat Sheet — compare all autoscalers	Compare HPA and VPA using “whereas” twice; then explain when KEDA is preferable in three sentences.
15	How do resource requests and limits affect scheduling?	All You Need to Know in 12 Minutes: Pods’ Requests and Limits in Kubernetes — follow the scheduling impact	How Kubernetes Requests and Limits Really Work — scheduling deep dive	Describe how the scheduler uses requests in four ordered sentences using “allocatable,” “fits,” and “binds.”
16	How do you profile and tune application performance?	Diagnosing Application Performance With EBPF, Pyroscope, and Kubernetes — watch the profiling workflow	Beyond Basic Scaling: Advanced Kubernetes Resource Strategies — advanced tuning	Write five sentences about continuous profiling using “flame graph,” “hot path,” and “in order to.”
17	How do you plan capacity for a growing cluster?	Tutorial: Kubernetes Smart Scaling: Getting Started with Karpenter — follow the node provisioning model	Getting the Most from Kubernetes Autoscaling — capacity planning tips	Explain how Karpenter differs from Cluster Autoscaler using “provisioning,” “node pool,” and “whereas.”
18	Review: design an autoscaling strategy for a web service	Kubernetes pod autoscaling for beginners — replay without captions	Kubernetes Autoscaling: Q&A With Fairwinds CTO Andy Suderman — verify your strategy	Give a two-minute explanation of an autoscaling strategy; compare with Day 13.

Week 4 — Progressive Delivery and Platform Engineering #

Day	Topic	Video	Article	English Practice
19	What is canary deployment and how does it reduce risk?	Progressive Delivery Made Easy With Argo Rollouts — follow the canary workflow	Progressive Delivery on OpenShift — canary and blue-green patterns	Explain canary deployment in four sentences using “traffic shifting,” “rollback,” and “risk.”
20	How does Argo Rollouts automate progressive delivery?	30 Days Of CNCF Projects \| Day 9: What is Argo Rollouts + Demo — follow the demo steps	More Problems with GitOps — and How to Fix Them — Argo Rollouts in GitOps	Describe how Argo Rollouts integrates with analysis templates using five sentences and “in order to.”
21	What is feature flagging and how does it decouple deploy from release?	Canary Deployments Are a Myth — True Progressive Delivery Occurs Via OpenFeature — follow the OpenFeature model	Feature Flags: Making Software Delivery Faster — feature flag fundamentals	Write five sentences about decoupling deploy from release using “feature flag,” “subset,” and “however.”
22	What is an internal developer platform and who builds it?	Creating Paved Paths for Platform Engineers — Panel — focus on the platform team roles	How Platform Engineering Enables the 10000-Dev Workforce — platform at scale	Explain what a paved path is in three sentences using “self-service,” “golden path,” and “in order to.”
23	How do you manage multi-cluster and multi-cloud reliability?	Seamless Multi-Cloud Kubernetes: A Practical Guide — follow the multi-cluster architecture	How Do the Internal Developer Platform and Portal Connect? — platform consistency	Compare single-cluster and multi-cluster reliability using “whereas” and “on the other hand.”
24	Review: design a progressive delivery pipeline	ArgoCon \| Argo Rollouts Update — Alexander Gaudreault — replay without captions	7 Major Gaps in Today’s GitOps Tools — verify your pipeline design	Give a two-minute explanation of a progressive delivery pipeline; compare with Day 19.

Week 5 — SRE at Scale: Cost, Culture, and Continuous Improvement #

Day	Topic	Video	Article	English Practice
25	How do you allocate cloud costs to teams (FinOps)?	FinOps Summit: Cost Visibility and Optimization in Kubernetes — follow the cost allocation model	FinOps: How Kubernetes Teams Can Best Work with Finance — FinOps fundamentals	Explain how FinOps allocates costs in four sentences using “attribution,” “showback,” and “in order to.”
26	What is an SRE culture and how does it differ from traditional ops?	How Google SRE and Developers Work Together \| GOTO 2021 — follow the collaboration model	SRE vs. DevOps? Successful Platform Engineering Needs Both — culture comparison	Compare SRE culture with traditional ops using “whereas,” “blameless,” and “error budget.”
27	How do you conduct a production readiness review?	Keynote: Beyond Operations: Scaling Platform Engineering in the CNCF Community — follow the platform maturity model	Kubernetes Isn’t Enough for a Production-Ready Platform — production readiness checklist	Write five sentences about production readiness using “observability,” “SLI,” and “in order to.”
28	What are the key metrics for platform team health?	Starting and Scaling a Platform Engineering Team — follow the team growth stages	Limitations in Measuring Platform Engineering with DORA Metrics — metrics beyond DORA	Describe three platform health metrics using “adoption,” “developer satisfaction,” and “whereas.”
29	How do you build a reliability-focused engineering culture?	Cloud Native SRE Practices in Financial Services — follow the real-world SRE adoption	What Platform Engineering Meant for Adidas’s SREs — SRE transformation story	Write 100 words about building reliability culture using “blameless,” “error budget,” and “continuous improvement.”
30	Final presentation: your SRE toolkit for cloud native	Jonathan Bryce, The Linux Foundation — replay the cloud native vision	The Evolution of the Site Reliability Engineer — revisit the SRE journey	Give a three-minute presentation connecting Weeks 1–5; compare your clarity with Day 01.

Weekly Self-Check #

After each sixth study day, record:

CNCF

Mon, 01 Jan 0001 00:00:00 +0000

Week 1 — CNCF Landscape and Core Concepts #

Day	Topic	Video	Article	English Practice
01	What is CNCF?	The Absolute Beginner’s Guide To Cloud Native — listen for the definition of cloud native	Introduction to Cloud Native Computing — definition	Record a 60-second baseline explanation of cloud native without a script.
02	How do containers differ from VMs?	Virtual Machines vs Containers - Simply Explained — follow the comparison	Containers vs. Virtual Machines: Another Perspective — comparison	Write three comparison sentences using “whereas” or “while.”
03	What does Kubernetes orchestrate?	What is Kubernetes - Kubernetes explained in 15 mins — listen for the orchestration role	What Is Container Orchestration? — how orchestration works	Explain the role of orchestration using “first,” “next,” and “finally.”
04	What is the container lifecycle?	How Pods and the Pod Lifecycle work in Kubernetes — follow the phases	What Do ‘Cloud Native’ and ‘Kubernetes’ Even Mean? — how pods fit into cloud native	Describe the lifecycle in four ordered sentences using “pending,” “running,” and “terminated.”
05	How does the CNCF landscape organize projects?	CNCF Landscape guided tour — follow the categories	An Introduction to the Cloud Native Landscape — landscape overview	Draw a simple map of the landscape layers and describe each aloud for 90 seconds.
06	Review: explain the CNCF cloud native stack	A Hitchhiker’s Guide to the CNCF Landscape — replay the overview	What Is ‘Cloud Native’ (and Why Does It Matter)? — verify your summary	Give a two-minute explanation of the cloud native stack; compare it with Day 01.

Week 2 — Orchestration and Runtime Mechanisms #

Day	Topic	Video	Article	English Practice
07	What is a Pod?	Kubernetes Explained in 9 Minutes — listen for what a Pod contains	Kubernetes Building Blocks: Nodes, Pods, Clusters — building blocks	Explain a Pod using one everyday analogy in three sentences.
08	How does declarative configuration work?	Kubernetes Components explained! — follow the YAML structure	From YAML to Platforms: The Kubernetes Deployment Journey — declarative manifests	Write five sentences describing what you declare, using “desired state” and “actual state.”
09	What is a Service and how does networking work?	Kubernetes Services explained — follow the service types	Kubernetes Ingress for Beginners — how traffic reaches pods	Compare ClusterIP, NodePort, and LoadBalancer using “whereas” twice.
10	How does scheduling decide where to run?	How Does the Kubernetes Scheduler Pick a Node? — follow the scheduling steps	A Deep Dive into Kubernetes Scheduling — how scheduling works	Describe the scheduling process in four ordered sentences.
11	What is a controller and how does it reconcile?	Kubernetes Deployment vs StatefulSet vs DaemonSet — follow the controller differences	How Kubernetes Deployments Work — how controllers reconcile	Explain the reconciliation loop using “desired state,” “actual state,” and “converge.”
12	Review: explain how a workload runs in Kubernetes	Kubernetes Crash Course for Absolute Beginners — replay without captions	How Do Applications Run on Kubernetes? — verify your summary	Give a two-minute explanation from YAML to running Pod; compare with Day 07.

Week 3 — Observability, Security, and Delivery #

Day	Topic	Video	Article	English Practice
13	What is a service mesh and why use one?	Istio Service mesh explained — listen for what the sidecar proxy does	Introduction to Service Mesh — definition	Explain the problem a service mesh solves in three sentences.
14	How does container networking work?	Kubernetes Network - Container Network Interface - What is CNI? — follow the CNI model	A Hacker’s Guide to Kubernetes Networking — how networking works	Describe pod-to-pod communication using “namespace,” “CNI,” and “route.”
15	What is GitOps and how does continuous delivery work?	What is GitOps, How GitOps works and Why it’s so useful — follow the GitOps workflow	Understanding GitOps: The Latest Tools and Philosophies — how GitOps works	Write five sentences about Git as the single source of truth using “however” once.
16	How do we monitor and observe a cluster?	Kubernetes Observability 101: Metrics, Logs, Dashboards, and Traces — follow the three pillars	How OpenTelemetry Works: Tracing, Metrics and Logs on Kubernetes — observability standards	Compare metrics, logs, and traces using “whereas” or “in contrast.”
17	What are the security boundaries in cloud native?	Kubernetes Security Best Practices you need to know — focus on the recommended practices	6 Kubernetes Security Best Practices — security checklist	Write a five-point security checklist using “must,” “should,” and “in order to.”
18	Review: explain observability, security, and delivery together	Kubernetes Tutorial for Beginners - FULL COURSE — replay selected segments	Do I Really Need Kubernetes? — verify your reasoning	Give a two-minute explanation connecting observability, security, and delivery; compare with Day 13.

Week 4 — Maturity, Trade-offs, and Summary #

Day	Topic	Video	Article	English Practice
19	How does CNCF graduate projects?	Crossing the Chasm - CNCF Project Maturity — follow the maturity stages	How a Project Graduates from the CNCF — graduation process	Describe the three maturity stages using “sandbox,” “incubating,” and “graduated.”
20	When is Kubernetes overkill?	Is Kubernetes Overkill? Why You Probably Don’t Need It — listen for the warning signs	Kubernetes Isn’t Always the Right Choice — when to avoid Kubernetes	Write 100 words about a scenario where Kubernetes would be overkill using “however.”
21	How do you choose a CNCF project?	Keynote: Turn Up the Heat - Driving Cloud Native Innovation — follow the decision framework	A CTO’s Guide to Navigating the Cloud Native Ecosystem — how to evaluate tools	Explain your evaluation criteria in five sentences using “in order to” and “depends on.”
22	What are the operational costs of cloud native?	From Complexity to Clarity: Engineering an Invisible Kubernetes — focus on cost and effort	Navigating the Trade-Offs of Scaling Kubernetes Dev Environments — cost and performance	Compare two trade-offs using “whereas” and “on the other hand.”
23	Building a recommendation for a cloud native stack	Keynote: Driving Cloud Native Innovation into the Future — follow the project choices	The Cloud Native Landscape: Platforms Explained — how to piece tools together	Write a 150-word recommendation using ideas from at least three weeks.
24	Final presentation and reflection	Kubernetes Zero to Hero: The Complete Beginner’s Guide — replay selected segments	10 Key Attributes of Cloud Native Applications — revisit the fundamentals	Give a three-minute presentation; compare your clarity with Day 01.

Weekly Self-Check #

After each sixth study day, record:

Schedule Skill

Mon, 01 Jan 0001 00:00:00 +0000

Schedule Skill — 生成新的学习计划 #

本文档定义在本目录生成一份新「英语学习计划」的标准方法：建议 4 周 × 每周 6 天（Day 01–24），按主题实际内容可长可短；每天一个 Topic + Video + Article + English Practice。

触发方式：用户按如下格式发起，收到即按本文生成学习计划：

请根据Skill和主题生成一个英语学习计划

Skill：skill.md
主题：CNCF

主题：新计划的主题，用作文件名 NN-<Topic>.md 中的 <Topic> 与 front matter title（示例为 CNCF，可替换为任意主题）
文件（可选）：仅指对话中明确指定为素材的文档（skill.md 本身是操作说明，不算素材），作为计划的内容起点；未提供时按第 5 节围绕 主题 自行调研

风格参考： 50-AI Topics.md —— 仅参考其表格风格、label/note 措辞与参考块写法；结构与标签以本文为准（建议 4 周、无每周基础对）。

0. 环境与前提 #

内容目录：content/docs/99-English/01-Schedule/（站点为中文，计划正文为英文）
构建验证：hugo --source /Users/jnh/workspaces/github/nianjiang.github.io --minify --renderToMemory --noBuildLock
网络搜索无需向用户确认，直接执行
只使用验证过的资源：视频 UP主/频道、文章 URL 必须逐条确认存在；无法确认归属的一律弃用重找
英语水平：B2（CEFR 中高级）；视频、文章与练习的难度应对齐此水平

1. 文件编号与骨架 #

文件名：NN-<Topic>.md，NN 取目录内下一个空闲 weight（50 = AI Topic，51 = CNCF，新计划从 52 起；若某 weight 已有占位 stub 文件，可直接填充或取下一个空闲号）
Front matter 只有 weight 与 title，样式同 50-AI Topics.md
Day 编号全文件连续，两位补零（按总天数顺延；建议 01–24）
文件顺序：front matter → ## Week 1…N（每周 6 行表）→ ## Weekly Self-Check → 参考块
无"每周基础对"：不写每周 Video: / Article: 声明行；每一天的视频与文章完全由当天 Topic 确定

2. 骨架模板 #

---
weight: NN
title: "<Topic>"
---

## Week 1 — <Theme>

| | Day | Topic | Video | Article | English Practice |
| :---: | :--- | :--- | :--- | :--- | :--- |
| | 01 | <疑问式主题> | [<Video title>][v01] — <聆听重点> | [<Article title>][a01] — <note> | <具体任务> |
| | 02 | … | [<Video title>][v02] — <聆听重点> | [<Article title>][a02] — <note> | … |
| | 06 | Review: <回顾主题> | [<Recap video>][v06] — <收尾重点> | [<Checklist article>][a06] — verify your … | <两分钟口头输出任务> |

## Week 2 — <Theme>
<!-- 同构：Day 07–12，标签 v07–v12 / a07–a12 -->

## Weekly Self-Check

<!-- 原样复制 50-AI Topics.md 的 5 条要点，不改动 -->

[v01]: <Day 01 视频 URL>
[v02]: <Day 02 视频 URL>
[a01]: <Day 01 文章 URL>
[a02]: <Day 02 文章 URL>

3. 主题递进（建议 4 周，按主题可长可短） #

总体弧线：领域总览与核心概念（周 1）→ 关键机制（周 2）→ 工程与应用（周 3）→ 风险、权衡与总结（周 4）；按主题调整，保持由浅入深、末周收束于"做选择 + 总复盘"
每周一个主题（## Week N — <Theme>）；建议 4 周，按主题实际内容可长可短（如 2 周、6 周、8 周均可），Day 编号顺延；扩展的周作为深化周（原理 / 实操 / 案例按主题定），总结周永远收尾
每周第 6 天固定为 Review：当天 Topic 以 Review: 开头，视频选该主题的回顾/收尾类内容，文章选"验证/检查"类，练习为两分钟口头输出（对照 Day 01 或前几周的清晰度）

4. 周表格规则 #

表头与对齐行必须逐字符一致（注意行首的空列）：

| | Day | Topic | Video | Article | English Practice |
| :---: | :--- | :--- | :--- | :--- | :--- |

标签与 Day 一一对应：Day 05 → v05 / a05；全文件 v01–vNN、a01–aNN（NN = 总天数），一一对应、无跳跃
Topic：简短的疑问式或动名词描述；第 6 天以 Review: 开头
Video 单元格：[<视频标题>][vNN] — <label>
- label 为小写动词短语，描述该视频的聆听重点而非时间戳：listen for … / follow the … / focus on … / watch how …
- 每天一个视频；仅当该主题确需补充时才加第二个视频：, <br/>[<另一个视频>][vNNb] — <label>
Article 单元格：[<文章标题>][aNN] — <note>
- note 描述该文内容并与当天 Topic 呼应：definition / how … works / benefits / challenges / risks and safeguards / verify your summary / check your design / fact-check your recommendation
English Practice：具体可执行、口语与写作交替；写入任务时附词数（80–150）或连接词要求（“whereas”、“however”、“in order to”）；第 6 天固定为两分钟口头输出类

5. 资源调研规则 #

文章：优先选定一个与主题匹配的权威文档家族（如 IBM Think、Kubernetes 官方文档、CNCF 项目页），每天的文章取自该家族的具体页面，正文必须为英文；逐条搜索确认页面真实存在（标题能对上）后才写入
视频：每天一个，来自与当天 Topic 匹配的优质创作者，逐日不同；音频必须为英文（中文字幕无妨；中文讲解类视频只能作补充，不能充当当天 Video）；来源优先级：B 站（bilibili.com）优先，其次 YouTube，其他优质中国视频网站也可选用；B 站链接用规范形式 https://www.bilibili.com/video/BV…，不用 b23.tv 短链
验证归属：B 站视频搜 BV号 + UP主，YouTube 视频搜 视频ID + channel 或 "完整标题" + channel name；搜索结果的侧栏推荐与相似标题视频可能污染归属判断，存疑就换关键词再搜一次，仍不明确则弃用
全局唯一：同一 URL 与同一视频 ID（YouTube 视频 ID / B 站 BV 号）在整个文件中只出现一次；同一 UP主/频道跨日复用是允许的
弃用候选时保留备选列表，避免重复搜索

6. 参考块 #

文件末尾逐行列出所有标签：先 v01–vNN 按 Day 顺序，再 a01–aNN 按 Day 顺序；一天有 vNNb 时紧随 vNN 之后
无孤儿标签（定义了但未使用）、无未定义标签（使用了但未定义）

7. 构建与完整性验证 #

每个 Plan 完成或每周写入后必须执行：