Ai For Presales And Solutions Architects
 

Become that Trusted Advisor for your customers in AI/ML solutions.
What you'll learn
Learn key AI concepts, common AI services, and practical approaches for integrating AI into solution designs
Learn how to translate business requirements into AI Solutions
Learn about Cloud AI services on AWS, GCP and Azure
Learn about Generative AI services (ChatGPT, Google Gemini, Claude AI, etic)
Learn about the AI project lifecycle and its phases
Learn about the four pillars of AI
Become an excellent AI solutions professional
Requirements
Access to cloud services on AWS, GCP and Azure
Access to ChatGPT, Claude AI, Google Gemini
Description
Welcome to AI for Presales and Solutions ArchitectsTarget 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 FramingData 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
Who this course is for
Solutions Architects, Technical Leads, and anyone involved in designing and implementing technical solutions who wants to understand how to leverage AI effectively.