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My AI Infrastructure Engineer Journey - Progress Tracker

Name: _________________________ Start Date: _________________________ Target Completion: _________________________ Learning Path: ☐ Complete Mastery ☐ Fast Track MLOps ☐ Platform Engineering ☐ LLM Specialist

📊 Overall Progress

Modules Completed: _____ / 10 Exercises Completed: _____ / 26 Estimated Progress: _____ % Hours Invested: _____ hours Target Role: _________________________

🎯 Learning Goals

Primary Goal

Technical Goals

Career Goals

  • Target Role: _________________________
  • Target Salary: _________________________
  • Target Company: _________________________
  • Timeline to Job-Ready: _________________________

📚 Module Progress

mod-101: Foundations (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
04 - Python Env Manager ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
05 - ML Framework Benchmark ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
06 - FastAPI ML Template ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Key Takeaways:

Challenges Overcome:

mod-102: Cloud Computing (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
01 - Multi-Cloud Cost Analyzer ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
02 - Cloud ML Infrastructure ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
03 - Disaster Recovery ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Cloud Accounts Setup:

  • ☐ AWS configured
  • ☐ GCP configured
  • ☐ Azure configured

Key Takeaways:

Cost Savings Insights:

mod-103: Containerization (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
04 - Container Security ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
05 - Image Optimizer ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
06 - Registry Manager ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Key Takeaways:

Security Improvements:

  • Vulnerabilities found: _____
  • SBOM generated: ☐ Yes ☐ No
  • Image size optimized by: _____ %

mod-104: Kubernetes (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
04 - K8s Cluster Autoscaler ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
05 - Service Mesh Observability ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
06 - K8s Operator Framework ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Service Mesh Choice: ☐ Istio ☐ Linkerd

Key Takeaways:

Custom Operator Built: _________________________

mod-105: Data Pipelines (2 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
03 - Streaming Pipeline Kafka ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
04 - Workflow Orchestration Airflow ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Key Takeaways:

Pipeline Performance:

  • Throughput achieved: _____________
  • Latency: _____________

mod-106: MLOps (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
04 - Experiment Tracking MLflow ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
05 - Model Monitoring Drift ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
06 - CI/CD ML Pipelines ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Key Takeaways:

MLOps Maturity: ☐ Level 0 ☐ Level 1 ☐ Level 2 ☐ Level 3

mod-107: GPU Computing (3 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
04 - GPU Cluster Management ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
05 - GPU Performance Optimization ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
06 - Distributed GPU Training ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

GPU Access: ☐ Local ☐ Cloud (AWS/GCP/Azure)

Key Takeaways:

Performance Improvements:

  • GPU utilization improved by: _____ %
  • Training speedup achieved: _____ x

mod-108: Monitoring & Observability (2 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
01 - Observability Stack ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
02 - ML Model Monitoring ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

Key Takeaways:

Monitoring Metrics:

  • Dashboards created: _____
  • Alerts configured: _____
  • MTTR achieved: _____________

mod-109: Infrastructure as Code (2 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
01 - Terraform ML Infrastructure ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
02 - Pulumi Multi-Cloud ML ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

IaC Tool Preference: ☐ Terraform ☐ Pulumi ☐ Both

Key Takeaways:

Infrastructure Deployed:

  • Clouds: ☐ AWS ☐ GCP ☐ Azure
  • Resources managed: _____

mod-110: LLM Infrastructure (2 exercises)

Exercise Status Started Completed Time Spent Difficulty (1-5) Notes
01 - Production LLM Serving ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________
02 - Production RAG System ☐ //__ //__ ___ hrs ☐☐☐☐☐ ________________

Module Completed: ☐ Yes ☐ No | Total Time: _____ hours

LLM Used: _________________________ Vector DB: ☐ ChromaDB ☐ Pinecone ☐ Weaviate ☐ Other: _________

Key Takeaways:

LLM Performance:

  • Throughput: _____ tokens/sec
  • Latency (p95): _____ ms
  • Cost per 1M tokens: $ _____

🏆 Milestones

Learning Milestones

  • Week 1: Completed mod-101 (Foundations)
  • Week 3: Completed mod-102 (Cloud Computing)
  • Week 5: Completed mod-103 (Containerization)
  • Week 7: Completed mod-104 (Kubernetes)
  • Week 9: Completed mod-105 (Data Pipelines)
  • Week 11: Completed mod-106 (MLOps)
  • Week 13: Completed mod-107 (GPU Computing)
  • Week 14: Completed mod-108 (Monitoring)
  • Week 15: Completed mod-109 (Infrastructure as Code)
  • Week 18: Completed mod-110 (LLM Infrastructure)
  • Final: All 26 exercises completed! 🎉

Skill Milestones

  • Deployed first multi-cloud infrastructure
  • Built first Kubernetes operator
  • Optimized GPU workload (>50% improvement)
  • Deployed production LLM serving
  • Implemented complete observability stack
  • Built end-to-end MLOps pipeline
  • Created production RAG system

💼 Portfolio Projects

Track projects built during the curriculum that showcase your skills.

Project Module Status GitHub Link Demo Link Notes
Multi-Cloud Cost Tool mod-102 ☐ __________ __________ __________
Container Security Scanner mod-103 ☐ __________ __________ __________
K8s Custom Operator mod-104 ☐ __________ __________ __________
Real-time ML Pipeline mod-105 ☐ __________ __________ __________
MLOps Platform mod-106 ☐ __________ __________ __________
GPU Cluster Manager mod-107 ☐ __________ __________ __________
Observability Stack mod-108 ☐ __________ __________ __________
Terraform ML Infra mod-109 ☐ __________ __________ __________
LLM Serving Platform mod-110 ☐ __________ __________ __________
Production RAG System mod-110 ☐ __________ __________ __________

Portfolio Repository: ___________________________________________________________ Portfolio Website: ___________________________________________________________

📖 Learning Journal

Week of //____

Modules/Exercises Worked On:

What I Learned:

Technical Challenges:

How I Overcame Them:

Aha Moments:

Questions to Explore:

Next Week's Goals:

Week of //____

Modules/Exercises Worked On:

What I Learned:

Technical Challenges:

How I Overcame Them:

Aha Moments:

Questions to Explore:

Next Week's Goals:

🎓 Certifications Planned

Cloud Certifications

  • AWS Certified Machine Learning - Specialty

    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

  • Google Cloud Professional ML Engineer

    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

  • Microsoft Azure AI Engineer Associate

    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

Kubernetes Certifications

  • Certified Kubernetes Administrator (CKA)

    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

  • Certified Kubernetes Application Developer (CKAD)

    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

Infrastructure & DevOps

  • HashiCorp Certified: Terraform Associate
    • Target Date: //____
    • Study Resources: _______________________
    • Practice Exams Completed: _____ / _____
    • Score: _______

Specialized

  • NVIDIA Deep Learning Institute - Fundamentals

    • Target Date: //____
    • Courses Completed: _______________________

  • MLOps Specialization (Coursera/DeepLearning.AI)

    • Target Date: //____
    • Courses Completed: _____ / 4

🌟 Skills Development

Rate your proficiency: 1=Beginner | 2=Intermediate | 3=Advanced | 4=Expert

Skill Before After Target Notes
Cloud Platforms
AWS ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
GCP ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Azure ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Multi-Cloud Strategy ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Containerization
Docker Advanced ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Container Security ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Registry Management ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Kubernetes
Advanced K8s ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Autoscaling (HPA/VPA/CA) ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Service Mesh ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Custom Operators ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Data Engineering
Kafka / Streaming ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Apache Airflow ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Spark ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
MLOps
MLflow ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Model Monitoring ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
CI/CD for ML ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
DVC / Data Versioning ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
GPU Computing
GPU Cluster Mgmt ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
GPU Optimization ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Distributed Training ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
CUDA / Low-level ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Monitoring
Prometheus ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Grafana ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Distributed Tracing ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
ELK Stack ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Infrastructure as Code
Terraform ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Pulumi ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
LLM Infrastructure
LLM Serving (vLLM) ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
RAG Systems ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
Vector Databases ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________
LLM Optimization ☐☐☐☐ ☐☐☐☐ ☐☐☐☐ ________________

💡 Resources Used

Communities

  • Kubernetes Slack (#sig-autoscaling, #sig-ml, #istio, etc.)
  • MLOps Community (Discord/Slack)
  • Reddit (r/mlops, r/kubernetes, r/MachineLearning, r/aws)
  • LinkedIn Groups: _______________________
  • Discord servers: _______________________

Books Read

Recommended:

  • "Building Machine Learning Powered Applications" by Emmanuel Ameisen
  • "Machine Learning Systems Design" by Chip Huyen
  • "Kubernetes Patterns" by Bilgin Ibryam
  • "Designing Data-Intensive Applications" by Martin Kleppmann

Online Courses

Mentors/Peers

Name Role How They Helped
____________ ____________ ________________________________
____________ ____________ ________________________________
____________ ____________ ________________________________

💰 Cost Tracking

Cloud Spending

Month AWS GCP Azure Total Notes
/ $__ $__ $__ $__ ________________
/ $__ $__ $__ $__ ________________
/ $__ $__ $__ $__ ________________

Total Cloud Costs: $ _____________ Budget: $ _____________ Remaining: $ _____________

Cost Optimization Tips Learned:

🎯 Job Application Tracker

Target Companies

Company Position Applied Interview Status Notes
____________ ____________ //__ //__ _______ __________
____________ ____________ //__ //__ _______ __________
____________ ____________ //__ //__ _______ __________
____________ ____________ //__ //__ _______ __________
____________ ____________ //__ //__ _______ __________

Application Stats:

  • Applications Sent: _____
  • Phone Screens: _____
  • Technical Interviews: _____
  • Offers: _____

Interview Preparation:

  • Resume updated with projects from this curriculum
  • LinkedIn profile optimized
  • Portfolio website live
  • GitHub repos polished and documented
  • System design practice (10+ problems)
  • LeetCode practice (50+ problems)
  • Mock interviews completed (3+)

Salary Negotiations

Target Salary: $ _____________ Offers Received:

  1. Company: ____________ | Amount: $ ____________ | Accepted: ☐
  2. Company: ____________ | Amount: $ ____________ | Accepted: ☐

🚀 Final Reflection

What Worked Well

What I'd Do Differently

Biggest Challenges

Most Valuable Learnings

Advice for Future Learners

Next Steps in My Career

📈 Progress Visualization

Curriculum Completion:
[████████████████████████████████████████          ] 80%

Module Breakdown:
mod-101 Foundations:          [█████████████████████████████] 100%
mod-102 Cloud Computing:      [█████████████████████████████] 100%
mod-103 Containerization:     [█████████████████████████████] 100%
mod-104 Kubernetes:           [███████████████████           ]  70%
mod-105 Data Pipelines:       [████████████████              ]  50%
mod-106 MLOps:                [███████████                   ]  33%
mod-107 GPU Computing:        [                              ]   0%
mod-108 Monitoring:           [                              ]   0%
mod-109 IaC:                  [                              ]   0%
mod-110 LLM Infrastructure:   [                              ]   0%

Skills Development:
Cloud Platforms:      [████████████████████████          ] 85%
Kubernetes:           [█████████████████████             ] 75%
Docker/Containers:    [██████████████████████████████    ] 95%
MLOps:                [███████████████                   ] 55%
GPU Computing:        [████████                          ] 30%
Monitoring:           [██████████████                    ] 50%
IaC:                  [██████████                        ] 35%
LLM Infrastructure:   [████                              ] 15%

⚡ Quick Stats

Last Updated: //____

This Week:

  • Exercises completed: _____
  • Hours studied: _____
  • Code commits: _____
  • Blog posts written: _____

All Time:

  • Total exercises: _____ / 26
  • Total hours: _____ / 240
  • Certifications earned: _____
  • Portfolio projects: _____
  • GitHub stars received: _____

Keep pushing forward! Every hour invested brings you closer to your ML Infrastructure Engineer goals! 🚀

You've got this! 💪

Last Updated: October 25, 2025 Version: 1.0