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Learning Roadmap

How to Become a AI Platform Strategist

A step-by-step, phase-based learning path from beginner to job-ready AI Platform Strategist. Estimated completion: 7 months across 4 phases.

4 Phases
30 Weeks Total
Medium Entry Barrier
Advanced Difficulty
← AI Platform Strategist Overview Interview Prep →
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  1. Foundations: AI/ML & Cloud Basics

    6 weeks
    Goals
    • Understand core ML concepts and the typical ML lifecycle.
    • Gain foundational knowledge of one major cloud provider's AI/ML services.
    • Learn basic scripting (Python) for data manipulation and API calls.
    Resources
    • Coursera: 'Machine Learning Specialization' by Andrew Ng
    • AWS Skill Builder: 'AWS Cloud Practitioner Essentials'
    • Fast.ai: 'Practical Deep Learning for Coders'
    Milestone

    Can articulate the difference between training and inference, and navigate the console of a cloud AI service like SageMaker.

  2. Deep Dive: Platform Ecosystems & Tools

    8 weeks
    Goals
    • Master the key services of AWS, GCP, and Azure for ML (SageMaker, Vertex AI, Azure ML).
    • Explore the open-source ecosystem: Hugging Face Transformers, LangChain, and MLOps tools.
    • Understand infrastructure as code (Terraform) and containerization (Docker, K8s) for AI workloads.
    Resources
    • A Cloud Guru / Pluralsight: Advanced cloud AI/ML courses
    • Official documentation: LangChain, Hugging Face, AWS Well-Architected Framework
    • Hands-on projects on Qwiklabs or Cloud-based IDEs
    Milestone

    Can design a high-level architecture diagram for a GenAI application using a mix of cloud services and open-source libraries, including cost and scalability considerations.

  3. Strategy, Business, & Governance

    10 weeks
    Goals
    • Learn frameworks for TCO, ROI, and business case development for technology investments.
    • Study AI governance principles (fairness, accountability, transparency) and relevant regulations.
    • Practice stakeholder communication, vendor negotiation, and strategic roadmapping.
    Resources
    • Book: 'The AI Organization' by David Carmona
    • Resources from the AI Governance Center (e.g., NIST AI RMF)
    • Case studies on platform migration and enterprise AI adoption
    • Practice business case templates from Harvard Business School Online
    Milestone

    Can draft a compelling 10-slide strategy deck recommending an AI platform stack for a hypothetical company, complete with roadmap, risks, and financial projections.

  4. Specialization & Portfolio Building

    6 weeks
    Goals
    • Deepen expertise in a high-demand vertical (e.g., financial services AI, healthcare AI).
    • Execute a capstone project simulating a real platform strategy engagement.
    • Build a portfolio of written analyses, architecture diagrams, and strategic documents.
    Resources
    • Industry reports from Gartner, Forrester on AI platforms
    • Public case studies from major cloud providers
    • Networking and engagement with AI strategy communities (e.g., specific LinkedIn groups)
    Milestone

    Possess a polished portfolio with 2-3 detailed case studies and be prepared to interview for AI Platform Strategist roles.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

AI Platform Vendor Scorecard & Recommendation

Intermediate

Define a weighted scoring matrix (cost, performance, ease of use, ecosystem) and evaluate 3 major cloud AI platforms (AWS, GCP, Azure) for a specific use case like image classification. Produce a formal recommendation report.

~30h
AI/ML Platform Architecture & EvaluationTechnical Business Case & ROI AnalysisVendor Management

GenAI Application Platform Blueprint

Advanced

Design the end-to-end platform architecture for a retrieval-augmented generation (RAG) chatbot. Include components for data ingestion, vector storage, LLM serving, monitoring, and cost estimation. Use architecture diagramming tools.

~40h
Cloud Service Provider EcosystemsOpen-Source AI Frameworks StrategyTotal Cost of Ownership (TCO) Modeling

AI Platform Migration Playbook

Advanced

Create a detailed playbook for migrating an existing on-prem ML workload (e.g., a Spark-based recommendation engine) to a managed cloud service (e.g., AWS EMR or Databricks on AWS). Include risk assessment, rollback plan, and cost comparison.

~35h
AI Governance & Compliance FrameworksRoadmap Planning & PrioritizationCross-functional Stakeholder Alignment

Internal AI Platform Developer Portal Design

Beginner

Using a tool like Notion or GitBook, design the structure and content for an internal developer portal for your organization's AI platform. Include quickstart guides, API documentation, and best practices.

~20h
Technical Writing & DocumentationCross-functional Stakeholder Alignment

Responsible AI Platform Governance Framework

Intermediate

Develop a policy document and a technical checklist for deploying AI models responsibly on a chosen platform. Cover bias testing, model explainability, data lineage tracking, and incident response.

~25h
AI Governance & Compliance FrameworksAI/ML Platform Architecture & Evaluation

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