AI for Presales and Solutions Architects

Become that Trusted Advisor for your customers in AI/ML solutions.

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AI for Presales and Solutions Architects

AI for Presales and Solutions Architects udemy course free download

Become that Trusted Advisor for your customers in AI/ML solutions.

Welcome to AI for Presales and Solutions Architects

Target Audience: Solutions Architects, Technical Leads, and anyone involved in designing and implementing technical solutions who wants to understand how to leverage AI effectively.


This course will help equip customer-facing solutions selling professionals with a foundational understanding of key AI concepts, standard AI services, and practical approaches for integrating AI into solution designs, enabling them to identify opportunities and effectively communicate with AI/ML teams.

In this vendor-agnostic course, we will cover AWS, GCP, and Azure services as well as Generative AI solutions such as ChatGPT, Gemini, Claude and CoPilot.

Become that Trusted Advisor for your customers in AI/ML solutions.


Module 1: AI Fundamentals for Architects and Engineers

1.1 Introduction: Why AI Matters for Solutions Architects (5 minutes)


  • The evolving landscape since AI is now a core component of modern solutions.

  • Practical implications for solution design.

  • Reasoning and understanding business problems that AI could solve.

1.2 Core AI Concepts Refresher 

  • Machine Learning (ML):

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning 

  • Neural Networks

  • Key applications

  • What it is and its disruptive potential.

  • Large Language Models (LLMs) and Their Role in Modern Applications.

1.3 The AI/ML Project Lifecycle from an SA Perspective


  • Identify the phases of the project lifecycle.

  • Problem Framing

  • Data Collection & Preparation

  • Model Training & Evaluation

  • Deployment & MLOps

  • Integration

Module 2: AI Services & Integration Patterns 

2.1 Overview of Cloud AI Services


  • Managed AI Services (PaaS/SaaS):

  • Vision: Image recognition, object detection, facial analysis 

  • Speech: Speech-to-text, text-to-speech 

  • Language: Natural Language Processing (NLP), sentiment analysis, entity extraction, translation 

  • Generative AI/LLMs: Highlighting managed API access

  • Forecasting/Recommendation:

  • When to use Managed Services vs. Custom ML Models

2.2 Common AI Integration Patterns and Data Considerations 


  • API-driven Integration: Calling managed AI services.

  • Asynchronous Processing

  • Batch Processing

  • Real-time Inference

  • Data governance, privacy, and security

  • Data pipelines for AI   

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