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Small Language Models (slms): Private Ai, Edge & Strategy
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 43m | Size: 1.66 GB [/center]
Compare SLMs vs LLMs. Understand Offline AI, Privacy, Quantization & Pruning. Evaluate models like Llama 3 or Gemma
What you'll learn
Understand the shift from giant LLMs to efficient SLMs like Phi-3 and Llama 3.2 to achieve 80% of the results with only 1% of the resources and costs.
Identify high-impact business use cases for SLMs in offline environments, mobile apps, and edge devices where privacy and low latency are critical.
Learn how model compression techniques like distillation, pruning, and quantization enable running advanced AI on local hardware without cloud dependency.
Build a professional business case for SLM implementation, comparing costs, performance, and risks to bridge the gap between business and IT teams.
Evaluate the competitive advantages of local AI deployment, focusing on data sovereignty, regulatory compliance (GDPR/HIPAA), and reduced cloud latency.
Master the selection criteria to choose the right model size and architecture based on specific project requirements, balancing accuracy and efficiency.
Explore practical tools like Ollama and LM Studio to run and test state-of-the-art small language models on standard laptops without programming.
Design a strategic roadmap for AI adoption that prioritizes specialized, sustainable, and cost-effective models over generic and expensive alternatives.
Requirements
Accessible to all levels: No prior experience in AI or data science is required; this course is designed for any professional who wants to understand the strategic and practical value of efficient AI without the technical jargon.
No programming required: This course is designed for professionals and decision-makers; you do not need to write code or have a technical background.
Basic AI curiosity: A general interest in how Artificial Intelligence is evolving beyond Large Language Models like ChatGPT.
Problem-solving mindset: Willingness to identify inefficiencies and costs in business processes that could be improved with efficient AI.
Description
Welcome to Small Language Models: The Efficient AI Revolution, a course designed to help you move from scale-driven thinking to efficiency-driven strategy. While Large Language Models (LLMs) like GPT-4 are powerful, they often come with high costs, heavy infrastructure requirements, and significant concerns regarding privacy and sustainability. This course explores a different approach that is increasingly relevant for organizations today: Small Language Models (SLMs). These systems, such as Microsoft's Phi-3, Google's Gemma, and Meta's Llama 3.2, are designed to be more efficient, controllable, and adaptable to real-world constraints.Throughout this program, you will learn the fundamental differences between giant LLMs and smart SLMs, understanding why "bigger" is not always "better" in a business context. We will demystify technical concepts like distillation, pruning, and quantization without the need for complex math, showing you exactly how these models are compressed to run on standard laptops and edge devices. You will discover how SLMs can be 10 to 100 times cheaper to deploy and operate while providing millisecond response times for real-time applications.A key focus of this course is the strategic advantage of local AI. You will explore high-impact use cases such as internal chatbots, offline document analysis, and privacy-sensitive assistants for healthcare and finance where data sovereignty is mandatory. We provide a clear decision framework to help you choose between SLMs, LLMs, or simple rules based on your specific volume and privacy needs. Finally, you will learn how to build a professional business case and work effectively with technical teams to land your first SLM project successfully. Whether you are a business leader, an entrepreneur, or an AI aspirant, this course will equip you with the tools to lead the next generation of purpose-built intelligence.
Who this course is for
AI Career Starters and Aspirants: Individuals beginning their professional journey who want to gain a competitive edge by mastering the next generation of efficient and sustainable AI technology.
Non-Technical Professionals: Anyone in marketing, sales, finance, or HR who wants to use AI locally for document analysis and automation without learning to code
IT and Data Teams: Technical profiles who want to bridge the communication gap with business departments by focusing on ROI and deployment efficiency.
Tech Enthusiasts: Curious learners who want to run state-of-the-art language models on their own laptops and explore the future of on-device intelligence.
Business Leaders and Executives: Decision-makers who need to understand how to reduce AI costs and risks while maintaining operational control.
Project and Product Managers: Professionals looking for efficient alternatives to expensive cloud-based models to build sustainable AI roadmaps.
Innovation and Strategy Consultants: Experts who want to advise clients on the latest shift from "bigger is better" to "smarter and more efficient" AI strategy.
Entrepreneurs and Startups: Founders who need to implement advanced AI features in their products with minimal infrastructure and near-zero query costs.
Privacy and Compliance Officers: Professionals concerned with data sovereignty who need to learn how to deploy AI without sending sensitive data to third-party servers.
Small Language Models (slms): Private Ai, Edge & Strategy
Published 1/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 43m | Size: 1.66 GB [/center]
Compare SLMs vs LLMs. Understand Offline AI, Privacy, Quantization & Pruning. Evaluate models like Llama 3 or Gemma
What you'll learn
Understand the shift from giant LLMs to efficient SLMs like Phi-3 and Llama 3.2 to achieve 80% of the results with only 1% of the resources and costs.
Identify high-impact business use cases for SLMs in offline environments, mobile apps, and edge devices where privacy and low latency are critical.
Learn how model compression techniques like distillation, pruning, and quantization enable running advanced AI on local hardware without cloud dependency.
Build a professional business case for SLM implementation, comparing costs, performance, and risks to bridge the gap between business and IT teams.
Evaluate the competitive advantages of local AI deployment, focusing on data sovereignty, regulatory compliance (GDPR/HIPAA), and reduced cloud latency.
Master the selection criteria to choose the right model size and architecture based on specific project requirements, balancing accuracy and efficiency.
Explore practical tools like Ollama and LM Studio to run and test state-of-the-art small language models on standard laptops without programming.
Design a strategic roadmap for AI adoption that prioritizes specialized, sustainable, and cost-effective models over generic and expensive alternatives.
Requirements
Accessible to all levels: No prior experience in AI or data science is required; this course is designed for any professional who wants to understand the strategic and practical value of efficient AI without the technical jargon.
No programming required: This course is designed for professionals and decision-makers; you do not need to write code or have a technical background.
Basic AI curiosity: A general interest in how Artificial Intelligence is evolving beyond Large Language Models like ChatGPT.
Problem-solving mindset: Willingness to identify inefficiencies and costs in business processes that could be improved with efficient AI.
Description
Welcome to Small Language Models: The Efficient AI Revolution, a course designed to help you move from scale-driven thinking to efficiency-driven strategy. While Large Language Models (LLMs) like GPT-4 are powerful, they often come with high costs, heavy infrastructure requirements, and significant concerns regarding privacy and sustainability. This course explores a different approach that is increasingly relevant for organizations today: Small Language Models (SLMs). These systems, such as Microsoft's Phi-3, Google's Gemma, and Meta's Llama 3.2, are designed to be more efficient, controllable, and adaptable to real-world constraints.Throughout this program, you will learn the fundamental differences between giant LLMs and smart SLMs, understanding why "bigger" is not always "better" in a business context. We will demystify technical concepts like distillation, pruning, and quantization without the need for complex math, showing you exactly how these models are compressed to run on standard laptops and edge devices. You will discover how SLMs can be 10 to 100 times cheaper to deploy and operate while providing millisecond response times for real-time applications.A key focus of this course is the strategic advantage of local AI. You will explore high-impact use cases such as internal chatbots, offline document analysis, and privacy-sensitive assistants for healthcare and finance where data sovereignty is mandatory. We provide a clear decision framework to help you choose between SLMs, LLMs, or simple rules based on your specific volume and privacy needs. Finally, you will learn how to build a professional business case and work effectively with technical teams to land your first SLM project successfully. Whether you are a business leader, an entrepreneur, or an AI aspirant, this course will equip you with the tools to lead the next generation of purpose-built intelligence.
Who this course is for
AI Career Starters and Aspirants: Individuals beginning their professional journey who want to gain a competitive edge by mastering the next generation of efficient and sustainable AI technology.
Non-Technical Professionals: Anyone in marketing, sales, finance, or HR who wants to use AI locally for document analysis and automation without learning to code
IT and Data Teams: Technical profiles who want to bridge the communication gap with business departments by focusing on ROI and deployment efficiency.
Tech Enthusiasts: Curious learners who want to run state-of-the-art language models on their own laptops and explore the future of on-device intelligence.
Business Leaders and Executives: Decision-makers who need to understand how to reduce AI costs and risks while maintaining operational control.
Project and Product Managers: Professionals looking for efficient alternatives to expensive cloud-based models to build sustainable AI roadmaps.
Innovation and Strategy Consultants: Experts who want to advise clients on the latest shift from "bigger is better" to "smarter and more efficient" AI strategy.
Entrepreneurs and Startups: Founders who need to implement advanced AI features in their products with minimal infrastructure and near-zero query costs.
Privacy and Compliance Officers: Professionals concerned with data sovereignty who need to learn how to deploy AI without sending sensitive data to third-party servers.
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