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AI Platform Engineering Roadmap

A comprehensive learning path to transition into AI/ML platform engineering, whether you're coming from traditional platform engineering, ML engineering, or starting fresh.

Overview​

AI Platform Engineering sits at the intersection of:

  • Platform Engineering: Building developer platforms
  • Machine Learning: Understanding ML workflows
  • Distributed Systems: Scaling AI workloads
  • Cloud Infrastructure: Managing expensive compute
graph TD
    A[Start] --> B{Background?}
    B -->|Platform Eng| C[Learn ML Fundamentals]
    B -->|ML Engineer| D[Learn Platform Engineering]
    B -->|New Grad| E[Learn Both Paths]
    
    C --> F[ML Tools & Frameworks]
    D --> G[Cloud & Kubernetes]
    E --> G
    
    F --> H[GPU Infrastructure]
    G --> H
    
    H --> I[ML Platforms]
    I --> J[LLM Infrastructure]
    J --> K[Production ML Systems]
    
    K --> L[AI Platform Engineer]

Learning Paths by Background​

Path 1: From Platform Engineering to AI​

Timeline: 4-6 months

Month 1: ML Fundamentals​

Week 1-2: Machine Learning Basics

  • Complete Andrew Ng's ML Course (Basics only)
  • Understand training vs inference
  • Learn about model types (supervised, unsupervised, reinforcement)
  • Practice with scikit-learn tutorials

Week 3-4: Deep Learning Essentials

  • Fast.ai Practical Deep Learning - Part 1
  • Understand neural networks, CNNs, RNNs, Transformers
  • Learn PyTorch or TensorFlow basics
  • Run models on Google Colab with GPUs

Resources:

Month 2: GPU Computing & Infrastructure​

Week 1-2: GPU Fundamentals

  • Learn CUDA basics and GPU architecture
  • Understand GPU memory management
  • Practice with nvidia-smi and GPU monitoring
  • Deploy a model on a cloud GPU instance

Week 3-4: Distributed Training

  • Learn data parallelism vs model parallelism
  • Practice with Horovod or PyTorch Distributed
  • Understand gradient accumulation and synchronization
  • Implement a simple distributed training job

Hands-On Project:

# Build a distributed training setup
# 1. Set up multi-GPU training locally or on cloud
# 2. Implement data parallel training
# 3. Add checkpointing and recovery
# 4. Monitor GPU utilization and optimize

Resources:

Month 3-4: ML Platforms & Tools​

Week 1-2: MLOps Tools

  • Learn MLflow for experiment tracking
  • Practice with Weights & Biases
  • Understand model versioning and registry
  • Implement CI/CD for ML

Week 3-4: Kubernetes for ML

  • Deploy Kubeflow on a cluster
  • Create custom operators for ML workloads
  • Implement GPU scheduling and sharing
  • Build a model serving pipeline

Week 5-6: Production ML Systems

  • Learn feature stores (Feast, Tecton)
  • Implement model monitoring
  • Practice A/B testing for models
  • Build end-to-end ML pipeline

Capstone Project: Build a complete ML platform that includes:

  • Data ingestion and preprocessing
  • Distributed training orchestration
  • Model registry and versioning
  • Automated deployment and serving
  • Monitoring and alerting

Path 2: From ML Engineering to Platform​

Timeline: 3-4 months

Month 1: Platform Engineering Fundamentals​

Week 1-2: Linux & Systems

  • Master Linux system administration
  • Learn shell scripting and automation
  • Understand networking basics
  • Practice system debugging

Week 3-4: Containers & Orchestration

  • Deep dive into Docker
  • Learn Kubernetes fundamentals
  • Deploy applications on K8s
  • Understand service mesh concepts

Resources:

Month 2: Cloud & Infrastructure as Code​

Week 1-2: Cloud Platforms

  • Get AWS/GCP/Azure certification
  • Learn cloud networking and security
  • Practice with managed services
  • Understand cost optimization

Week 3-4: IaC and GitOps

  • Master Terraform for infrastructure
  • Learn Helm for Kubernetes packages
  • Implement GitOps with ArgoCD
  • Build reproducible environments

Hands-On Project: Deploy a complete ML platform using IaC:

# Terraform configuration for ML platform
module "ml_platform" {
  source = "./modules/ml-platform"
  
  gpu_node_pools = {
    training = {
      machine_type = "n1-highmem-8"
      accelerator_type = "nvidia-tesla-v100"
      accelerator_count = 2
      node_count = 3
    }
  }
  
  enable_features = {
    kubeflow = true
    mlflow = true
    prometheus = true
  }
}

Month 3: SRE & Production Operations​

Week 1-2: Monitoring & Observability

  • Set up Prometheus and Grafana
  • Implement distributed tracing
  • Create SLOs for ML services
  • Build alerting rules

Week 3-4: Reliability Engineering

  • Learn chaos engineering principles
  • Implement circuit breakers
  • Practice incident response
  • Build runbooks for ML systems

Path 3: Fresh Graduate Path​

Timeline: 6-8 months

Phase 1: Foundations (Month 1-2)​

Parallel Learning Approach:

  • Morning: Platform engineering basics
  • Afternoon: ML fundamentals
  • Evening: Hands-on projects

Week 1-4: Core Skills

  • Python programming proficiency
  • Linux command line mastery
  • Basic ML algorithms
  • Container basics

Week 5-8: Applied Learning

  • Deploy ML models in containers
  • Basic Kubernetes operations
  • Simple neural networks
  • Cloud platform basics

Phase 2: Specialization (Month 3-5)​

Choose a focus area and deep dive:

Option A: Infrastructure Focus

  • Advanced Kubernetes
  • GPU cluster management
  • Performance optimization
  • Cost management

Option B: ML Systems Focus

  • MLOps pipelines
  • Model serving at scale
  • Feature engineering systems
  • Experiment tracking platforms

Phase 3: Integration (Month 6-8)​

Combine all skills in real projects:

  • Build an end-to-end ML platform
  • Contribute to open source projects
  • Complete internship if possible
  • Build portfolio projects

Skill Progression Checklist​

Beginner Level ✅​

  • Understand ML training vs inference
  • Deploy models in containers
  • Basic Kubernetes operations
  • Simple GPU utilization
  • Use cloud ML services

Intermediate Level 🚀​

  • Build distributed training pipelines
  • Implement model serving with scaling
  • Create custom Kubernetes operators
  • Optimize GPU utilization
  • Design multi-tenant ML platforms

Advanced Level 🌟​

  • Architect large-scale ML systems
  • Implement advanced scheduling algorithms
  • Build custom ML infrastructure tools
  • Optimize costs at scale
  • Lead platform engineering teams

Project Ideas by Difficulty​

Beginner Projects​

1. ML Model API Service

# Build a Flask API that serves a pretrained model
# Add Docker containerization
# Deploy on Kubernetes
# Add basic monitoring

2. GPU Monitoring Dashboard

  • Collect GPU metrics using nvidia-smi
  • Store in Prometheus
  • Visualize in Grafana
  • Add alerting for high usage

3. Simple ML Pipeline

  • Data preprocessing job
  • Training job with checkpointing
  • Model evaluation
  • Automated deployment

Intermediate Projects​

1. Distributed Training Platform

  • Multi-node training orchestration
  • Dynamic resource allocation
  • Fault tolerance and recovery
  • Performance monitoring

2. Feature Store Implementation

  • Real-time feature serving
  • Batch feature computation
  • Feature versioning
  • Integration with training

3. Model Serving Optimization

  • Implement batching strategies
  • Add caching layers
  • Auto-scaling based on load
  • A/B testing framework

Advanced Projects​

1. Multi-Tenant ML Platform

  • Resource isolation and quotas
  • Cost attribution per team
  • Shared model registry
  • Platform APIs and SDKs

2. LLM Serving Infrastructure

  • Implement model parallelism
  • Optimize memory usage
  • Build request routing
  • Add semantic caching

3. Edge AI Platform

  • Model compression pipeline
  • Edge device management
  • Over-the-air updates
  • Federated learning setup

Certifications and Credentials​

Cloud Platforms:

  • 🎓 AWS ML Specialty
  • 🎓 Google Cloud ML Engineer
  • 🎓 Azure AI Engineer

Kubernetes:

  • 🎓 CKA (Certified Kubernetes Administrator)
  • 🎓 CKS (Certified Kubernetes Security Specialist)

NVIDIA:

  • 🎓 Deep Learning Institute Certifications
  • 🎓 CUDA Programming Certification

Alternative Credentials​

Online Courses with Certificates:

  • Fast.ai Practical Deep Learning
  • Full Stack Deep Learning
  • MLOps Specialization (Coursera)

Open Source Contributions:

  • Kubeflow contributors
  • Ray contributors
  • MLflow contributors

Study Resources by Topic​

GPU and CUDA Programming​

Distributed Systems for ML​

MLOps and Platforms​

Production ML Systems​

Career Milestones​

0-1 Year: Foundation Building​

  • Deploy first ML model to production
  • Contribute to platform tools
  • Understand GPU utilization
  • Basic cost optimization

1-3 Years: Platform Development​

  • Design ML pipelines
  • Build platform features
  • Lead small projects
  • Mentor juniors

3-5 Years: Architecture & Leadership​

  • Architect large systems
  • Define platform strategy
  • Lead team initiatives
  • Speak at conferences

5+ Years: Strategic Impact​

  • Drive organizational ML strategy
  • Build new platform capabilities
  • Influence industry standards
  • Lead platform teams

Continuous Learning Plan​

Daily Habits (30 min/day)​

  • Read one ML/Platform engineering article
  • Review code from ML infrastructure repos
  • Practice with new tools
  • Engage in community discussions

Weekly Goals (5 hours/week)​

  • Complete one hands-on tutorial
  • Contribute to open source
  • Write about learnings
  • Attend virtual meetups

Monthly Objectives​

  • Complete a small project
  • Read a technical book chapter
  • Get feedback on work
  • Update learning plan

Quarterly Milestones​

  • Finish a certification/course
  • Present at team meeting
  • Complete significant project
  • Evaluate career progress

Community and Networking​

Online Communities​

Conferences and Events​

  • 🎯 KubeCon (Platform track)
  • 🎯 NeurIPS (Systems for ML workshop)
  • 🎯 MLOps World
  • 🎯 Ray Summit

Local Meetups​

  • Search for ML/AI meetups
  • Platform engineering groups
  • Cloud user groups
  • GPU computing meetups

Final Tips​

  1. Build in Public: Share your projects and learnings
  2. Focus on Fundamentals: Don't chase every new tool
  3. Get Hands-On: Theory without practice won't help
  4. Find Mentors: Connect with AI platform engineers
  5. Stay Curious: The field evolves rapidly

Remember: AI Platform Engineering is a marathon, not a sprint. Focus on consistent progress and practical experience. The combination of platform engineering and ML expertise makes you uniquely valuable in the current market.