From Curiosity to Confidence: AI Fundamentals for Technical Architects, Consultants, and MVPs

Will you lose that next great opportunity because you don't have AI skills? Are you ready for a future that is coming quickly? Let's keep that from happening!

What's coming after Copilots

Imagine humans collaborating in real time, with an AI agency, a self-organizing team of expert AI agents. This human-AI team can solve in minutes what takes your competition days or weeks. Are you ready for this?

Learn what tools are available now

Elevate your Azure AI skills beyond Copilot with our comprehensive course, designed for those who are at the forefront of the AI revolution.

You'll gain a solid understanding of Azure AI beyond Copilot for success. Dive into the Azure AI toolkit, mastering the essentials for effective application across your projects. This course not only tackles common challenges faced with Copilot and LLMs but also covers the eight key design considerations for your AI initiatives. I'll share lessons learned from helping Fortune 500 customers implement solutions in AI.

And what's coming soon

Learn about some mind-blowing AI breakthroughs predicted to revolutionize the field in the next 12-24 months. You'll learn about agents, agencies and swarms and how these will change how work gets done.

Stay ahead of the curve, register today

Your journey to Azure AI mastery begins here.

Class begins May 15.

Training Outcomes


  • Understanding of Basic Terms: Grasp the basic concepts of ML, DL, and AI, including how they differ from each other.
  • Neural Networks: Gain knowledge about what neural networks are and their significance in AI.
  • Training Concepts: Learn what training, fine-tuning, and grounding mean in the context of AI development.
  • Multi-Modality: Understand the concept of multi-modality in AI systems.

Type of AI

  • Generative AI: Understand what generative AI is, its history, its relationship to other technologies, and how it's different from other forms of AI.
  • Machine Learning and its Types: Deepen your understanding of machine learning, computer vision, OCR (Optical Character Recognition), NLP (Natural Language Processing), and Document AI.

Types of AI Approaches

  • Approaches to AI: Learn about different AI approaches including generative, adversarial, and cooperative methodologies.

Generative AI in Detail

  • Functionalities and Limitations: Understand the functionalities offered by generative AI, such as Q&A systems, agents/assistants, content generation and analysis, and the concept of hallucinations in AI outputs.
  • Technical Concepts: Grasp the workings of large language models (LLMs), vectorization, embeddings, vector databases, and RAG (Retriever-Augmented Generation).
  • Prompt Engineering and Security: Learn about prompt engineering and security concerns like prompt injection.
  • Applications: Understand how generative AI is used in various applications like copilots (e.g., ChatGPT), agents/assistants, and common uses.

Machine Learning in Detail

  • Foundational Knowledge: Get a detailed understanding of machine learning, including its definition, functioning, and categories (supervised, unsupervised, and reinforcement learning).
  • Techniques and Uses: Learn about classifications, deep learning, and common uses in various domains.

Computer Vision in Detail

  • Understanding Computer Vision: Understand the definition, how image/video processing works, the role of convolutional neural networks (CNNs), transformers, multi-modal models, and GANs.
  • Applications: Learn about the common applications of computer vision technologies.

NLP in Detail

  • Deep Dive into NLP: Understand the processes behind natural language understanding, natural language generation, their workings, and common uses.

Document AI in Detail

  • Understanding Document AI: Learn the differences between Document AI and OCR, how Document AI works, including structured and unstructured document extraction, and its common uses.

Considerations for Implementation

  • Implementation Pillars: Understand the eight pillars of implementation consideration, differences between content and structure data, search issues, and the importance of data quality and freshness.
  • Integration: Understand opportunities in the Microsoft ecosystem for integration with Office 365, Fabric, Teams, and more.

Opportunities for Monetization with AI

  • Monetization Strategies: Gain insights into how AI technologies can be monetized, covering various business and operational models.

Future AI developments

  • New AI techniques: Use of agents in novel way to increase productivity/creativity.
  • Enhancements to understanding: New techniques to improve RAG performance.
  • Latest AI developments

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