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   

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Material Download

Section 2: Module 1 -Core AI Fundamentals for Solutions Architect

Lecture 3 Module One Overview

Lecture 4 1.1 Why AI Matters Now More than ever for SA's

Lecture 5 Discussion - Importance of AI/ML in Sales and Solutions Engineering

Lecture 6 AI in Business Processes

Lecture 7 Discussion: Expectations and Outcomes

Lecture 8 Benefits of AI/ML for Business

Lecture 9 1.2 Core AI Concepts

Lecture 10 What is AI and ML

Lecture 11 What are the Machine Learning Approaches?

Lecture 12 Whiteboard - How AI works

Lecture 13 Compare and Contrast AI/ML

Lecture 14 Deep Learning

Lecture 15 Natural Language Processing (NLP)

Lecture 16 Generative AI, Predictive AI, Agentic AI, and others.

Lecture 17 Understanding Algorithms

Lecture 18 1.3 SA Perspective - Project Lifecycle and Value Proposition

Lecture 19 Whiteboard - AI ML Project Walkthru

Lecture 20 4 Pillars of AI Strategy

Lecture 21 Importance of data in AI/ML solutions

Lecture 22 Whiteboard - Presenting a Value-Driven Proposition and Roadmap

Lecture 23 Demonstration - Google Cloud Value Proposition

Section 3: Module 2. AI Services and Integration Patterns

Lecture 24 Module Two Overview

Lecture 25 2.1 Overview of AI Cloud Services

Lecture 26 Key Players in the Market

Lecture 27 Cloud Key Players in the Market

Lecture 28 Trends and Advancements

Lecture 29 Whiteboard - AI/ML Use Case - AWS ML Flow

Lecture 30 2.2 AI Integration Patterns and Data Considerations

Lecture 31 Data Sources and APIS

Lecture 32 Open Source Libraries

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