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:
- Listening: Can I identify the main point and three supporting details without captions?
- Vocabulary: Can I use five useful phrases from this week in new sentences?
- Speaking: Can I explain the topic for two minutes using only a few keywords?
- Writing: Can I produce a short summary with a main point, an example, and a limitation?
- Next step: Which one difficulty should I focus on next week? Repeat a difficult week if needed; finishing on schedule is not a language-proficiency test.