AI for Presales and Solutions Architects

Name: AI for Presales and Solutions Architects
Rating: 3.3 (9 reviews)

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

Created byJoseph Holbrook

Last updated 8/2025

English

English [Auto],

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

Course content

3 sections • 42 lectures • 4h 12m total length

Introduction5:00
Explore how AI and ML apply to solutions architects serving enterprise customers, covering AI services, integration patterns, and key tools like AWS, Google, AutoML, Gemini, and Vertex AI.
Course Material Download0:24
Download course content from link

Module One Overview1:00
Explore why ai matters now, define what ai is and its types, and map ai use scenarios to project discussions and implementation timelines for presales and solutions architects.
1.1 Why AI Matters Now More than ever for SA's0:37
Explore why AI and ML matter for presales and solutions architects, and map their role in sales, business processes, and SaaS outcomes, highlighting key benefits.
Discussion - Importance of AI/ML in Sales and Solutions Engineering3:22
AI in Business Processes3:03
Discussion: Expectations and Outcomes5:15
Benefits of AI/ML for Business11:39
1.2 Core AI Concepts1:12
Discover core ai concepts, differentiate ai from ml, explore deep learning and natural language processing, and examine generative ai, predictive agentic types, and data's value.
What is AI and ML6:35
What are the Machine Learning Approaches?9:35
Explore machine learning approaches: supervised, unsupervised, and reinforcement learning, and learn how labeled versus unlabeled data guides model selection with examples from image classification, stock prices, and customer segmentation.
Whiteboard - How AI works4:55
Explore how AI chatbots like Bard and Gemini work on a whiteboard, outlining NLP-driven input understanding, live internet access, data sources, and the two training stages.
Compare and Contrast AI/ML2:18
Compare AI and ML to clarify definitions, scope, goals, and examples, and review when AI, ML, or deep learning best apply in robotics, spam detection, fraud, and autonomous driving.
Deep Learning6:58
Natural Language Processing (NLP)5:43
Generative AI, Predictive AI, Agentic AI, and others.11:27
Understanding Algorithms6:33
Explore what an algorithm is in AI, including supervised, unsupervised, and reinforcement learning, and compare neural networks, deep learning, and transformer architectures with attention for pre-sales and solutions architects.
1.3 SA Perspective - Project Lifecycle and Value Proposition1:00
Adopt the solutions architect perspective on the AI lifecycle. Explore the four pillars of AI strategy, data in AI and ML, and Google Cloud service demonstrations.
Whiteboard - AI ML Project Walkthru15:39
4 Pillars of AI Strategy4:59
Explore Gartner's four pillars of AI strategy—vision, value realization, risk management, and adoption plans—and learn to map AI use cases, quantify ROI, identify risks, and craft adoption roadmaps.
Importance of data in AI/ML solutions7:17
Whiteboard - Presenting a Value-Driven Proposition and Roadmap14:46
Learn to translate AI into business value by creating a value proposition and roadmap, visualizing ROI (measurable, strategic, and capability), then quantify and summarize with stakeholder-focused visuals.
Demonstration - Google Cloud Value Proposition6:59
Discover Google Cloud AI and ML with Vertex AI, a unified platform for built-in training, notebooks, deployment, pre-trained models, and model registry.
Module One Review3:47
Explore core artificial intelligence concepts, including machine learning types, deep learning with neural networks, generative AI, predictive AI, and four pillars of an AI project.

Module Two Overview1:01
2.1 Overview of AI Cloud Services1:18
Key Players in the Market2:39
Identify the key players in the ai market, from tech giants with ai services to cloud providers, universities, OpenAI, Anthropic, XAI, grok, and startups, noting hardware, models, and use-case focuses.
Cloud Key Players in the Market3:27
Generative AI Services9:09
Vision, Speech, Language (Perceptual AI)10:16
Explore the perceptual pillars of ai—vision ai, speech ai, and language ai—and their integration across cloud services, with an AWS use case featuring AWS Translate, AWS Comprehend, and Amazon Connect.
Trends and Advancements4:53
Stay ahead as a solutions architect by mastering AI and ML trends, including generative AI, AI reasoning, explainable AI, custom models, governance, and enterprise-focused applications.
Whiteboard - AI/ML Use Case - AWS ML Flow6:54
Demonstration - Prompting for Solutions Architect13:40
Explore prompting techniques to interface with AI models, compare vendors with competitive analysis prompts, and role-play as a solutions architect to craft tailored outputs for data storage scenarios.
2.2 AI Integration Patterns and Data Considerations1:03
Data Sources and APIS8:56
Identify data sources and APIs for ai and ml, from open data portals and cloud marketplaces to academic datasets, synthetic data, and web scraping, with data preparation for training.
Open Source Libraries2:31
Explore open source libraries and non-proprietary software, and learn how free licenses and strong communities enable quick, cost-effective AI tools like TensorFlow, PyTorch, NLTK, Spacy, and Hugging Face for NLP.
General Integration Patterns11:06
AI Data governance, privacy, and security10:06
Explore AI data governance, privacy, and security, covering data quality, lineage, access controls, data minimization, de-identification, differential privacy, encryption, and enterprise compliance.
AI Integration Patterns12:11
Whiteboard - AI Integration Patterns AWS7:56
Explore AWS AI integration patterns, including direct service, batch processing, and event driven workflows, using Lambda, Step Functions, EventBridge, SageMaker for model deployment and Rekognition for media analysis.
Module Summary Review3:42
Certifications1:39
Explore essential cloud and AI certifications for presales and architects, including the Certified Cloud AI Solutions Architect, CPSA, Responsible AI Ethics Officer, and Federal Cloud Solutions Architect.

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 Architects

The AI arms race is on. Your company is asking for proposals. Your team is demanding AI-savvy architects. However, the gap between understanding that “AI is cool” and integrating AI into real cloud solutions is substantial.

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

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.

AI for Presales and Solutions Architects

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 5min

Module 1 -Core AI Fundamentals for Solutions Architect22 lectures • 2hr 15min

Module 2. AI Services and Integration Patterns18 lectures • 1hr 52min

Requirements

Description

Who this course is for: