- Module 1: The AI and platform engineering landscape
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The AI and platform engineering landscape
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Key definitions for platform engineering
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The growing impact of AI on software delivery
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The two pathways: Differentiating AI for PE and PE for AI
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Current state analysis and emerging trends
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Next steps
- Module 2: AI for platform engineering: Enhancing productivity and automation
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AI for platform engineering: Enhancing productivity and automation
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Intelligent use cases: From proactive observability to self-optimization
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Best practises: Mitigating risks and adopting AI responsibly
- Module 3: Platform engineering for AI: Building the backbone
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Platform engineering for AI: Building the backbone
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Architectural planes of an AI-focused IDP
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Streamlining delivery and scaling AI workloads
- Module 4: Implementing and scaling AI platforms: Best practices and future outlook
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Implementing and scaling AI platforms: Best practices and future outlook
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Best practices for responsible AI adoption
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Key future trends in platform engineering and AI
- Course feedback survey
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Feedback survey
Intro to AI in Platform Engineering
AI is changing how software teams build and run systems, but without strong platform engineering foundations organizations cannot scale it safely or effectively. This course gives you a clear, practical understanding of how AI improves platform capabilities, why platform engineering is becoming the backbone of AI initiatives, and the key practices you need to work confidently in this fast moving space.
What you'll learn
During this course, you'll learn:
How AI improves platform engineering through automation, observability, and smarter operations Why platform engineering is essential for running AI and ML workloads at scale The core risks of AI adoption and how to apply guardrails and human in the loop practices The key components of an AI-ready Internal Developer Platform and how golden paths support different engineering personas
Curriculum
4 MODULESMODULE 1
The AI and platform engineering landscape
Key definitions for platform engineering The growing impact of AI on software delivery The two pathways: Differentiating AI for PE and PE for AIMODULE 2
AI for platform engineering: Enhancing productivity and automation
How AI enhances productivity and automation Intelligent use cases: From proactive observability to self-optimization Best practices: Mitigating risks and adopting AI responsiblyMODULE 3
Platform engineering for AI: Building the backbone
Platforms for AI and ML workloads Architectural planes of an AI-focused IDP Streamlining delivery and scaling AI workloadsMODULE 4
Implementing and scaling AI platforms: Best practices and future outlook
Outlook on how to implement and scale AI platforms Best practices for responsible AI adoption Key future trends in platform engineering and AISurvey
Course feedback survey
Course feedback surveyMeet your Instructor
Mallory Haigh
Course instructor and Platform Engineering SME
Full-stack engineer by background (LAMP stack veteran + PHP lifer) - Also experienced in: Engineering management, customer success, product development
- Platform Engineering SME, course instructor, trainer, and coach
- #horsegirl, farmer, cat+dog mom
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