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Blog Research Assignment: AI Language Models for Business Leaders and Managers

Course: AML-3304 - AI Application Engineering Instructor: Peter Sigurdson Submission: Publish your report on LinkedIn

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Lab Overview

This lab workbook will guide you through the steps of generating a comprehensive AI report for business leaders and managers. Your report will introduce AI Language Models, their hosting options, cost analysis, implementation strategies, and workflow representations. The final deliverable will be posted on your LinkedIn blog using Stempad.com for content assembly.

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Lab Objectives

Understand AI Model Hosting Options: Explore cloud-hosted and bare-metal solutions for AI deployment.

Survey Popular AI Language Models: Compare token cost, inference cost, and use cases for LLMs and SLMs.

Develop a Workflow for AI Implementation: Represent how business leaders can integrate AI into their enterprise.

Utilize AI Assistants for Research & Report Generation: Use tools like ChatGPT, Claude, Perplexity, and You.com to gather insights.

Assemble the Report using Stempad and publish it on LinkedIn.

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Step 1: AI Model Hosting Options

AI models require different hosting solutions depending on performance, scalability, and cost considerations. Below are the three primary hosting options:

1.1 Cloud-Hosted Solutions

Google Cloud AI Platform: Supports TensorFlow and PyTorch models.

AWS SageMaker: Automates model deployment and scaling.

Microsoft Azure AI: Supports ML model training and inference.

1.2 Cloud Bare-Metal Servers

Offers high-performance GPU access without virtualization overhead.

Example: IBM Cloud Bare Metal, Oracle Cloud Bare Metal.

1.3 On-Premises Hosting

Requires local GPUs and infrastructure.

Suitable for enterprises prioritizing data privacy and regulatory compliance.

Task

Research Google Cloud, AWS, Azure, Hugging Face Spaces, and RunPod.io for AI hosting.

Compare costs, performance, and security concerns.

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Step 2: Survey of AI Language Models

LLMs (Large Language Models) and SLMs (Small Language Models) differ in computational requirements, costs, and ideal use cases.

Table 1

Table 1

Model

Tokens (Cost per 1K)

Inference Cost

Best Use Cases

GPT-4 (OpenAI)

$0.03 - $0.06

High

Chatbots, Research

Claude-2 (Anthropic)

Variable

Moderate

Business Docs, Reports

LLaMA-2 (Meta)

Open-source

Low

Custom AI Apps

Mistral-7B

Free/Open-source

Low

Lightweight AI Solutions

There are no rows in this table

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Task

Visit Hugging Face to explore open-source models.

Compare costs using OpenAI’s pricing calculator.

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Step 3: AI Implementation Workflow for Business Managers

Business leaders must follow a structured approach to integrate AI into their enterprise.

3.1 Steps for AI Implementation

Identify Business Needs (Customer support automation, predictive analytics).

Select the Right Model (Fine-tuning LLaMA-2 vs. using GPT-4 API).

Choose a Hosting Solution (Cloud-hosted vs. bare-metal).

Deploy the Model (Using Hugging Face Spaces, RunPod.io, or Azure).

Monitor Performance & Costs.

3.2 Representation of the AI Workflow

Task

Create a workflow diagram using draw.io or Lucidchart to visualize AI integration.

Reference the Unified Model Engineering Process (UMEP).

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Step 4: AI Research using Assistants

Use the following AI tools to collect information:

You.com: Summarizes academic papers.

ChatGPT.com: Answers complex AI queries.

Claude.ai: Processes long documents efficiently.

Perplexity.ai: Provides real-time AI knowledge.

Sample Prompts

"Compare the costs of hosting AI models on AWS, Azure, and Google Cloud."

"What are the advantages of using open-source models like Mistral-7B over GPT-4?"

"How can a business integrate AI for customer service automation?"

Task

Use at least two AI assistants to gather insights.

Save responses for reference.

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Step 5: Assembling the Report using Stempad

5.1 Using Stempad for Content Assembly

Create a new document on Stempad.com.

Structure it as:

Executive Summary

Introduction to AI Language Models

Model Hosting Options

Cost Analysis of LLMs & SLMs

AI Implementation Workflow

Conclusion & Recommendations

Task

Write and structure your AI Business Report on Stempad.

Format using headers, bullet points, and tables.

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Step 6: Publishing the Report on LinkedIn

6.1 How to Post on LinkedIn

Paste your Stempad report into the editor.

Add relevant hashtags: #AI #BusinessLeadership #MachineLearning.

Publish and share it with peers.

Task

Take a screenshot of your LinkedIn post.

Submit it as proof of completion.

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Final Deliverables

AI Business Report (LinkedIn Post).

Workflow Diagram (Uploaded to LinkedIn).

Cost Comparison Table (Included in the report).

Deadline: Week 7

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Grading Criteria

Table 2

Table 2

Component

Marks

AI Model Hosting Research

20%

LLM & SLM Survey

20%

Business AI Workflow

20%

AI Assistant Research

15%

Stempad Report Assembly

15%

LinkedIn Post & Engagement

10%

There are no rows in this table

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Resources

AML-3304 Course Material.

Hugging Face Spaces for AI Model Hosting.

Cloud AI Pricing Calculators (AWS, Azure, Google Cloud).

Stempad for Report Writing.

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Instructor Notes

💡 “AI isn’t just for developers—business leaders must understand its potential. Use this lab to create a practical, insightful guide for executives!” 🚀

Good luck! 🎯

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