Developing and Deploying AI Applications
Course 2328
3 DAY COURSE

In this hands-on Developing and Deploying AI Enabled Applications course we will explain how AI and AI Agents can be integrated into applications across a wide spectrum of AI use cases and scenarios including natural language processing, vision, and speech recognition. 

You will build AI enabled applications and Agents for applications such as chatbots that access your organizational data and greatly improve customer experience and organizational productivity.  You will explore issues such as security and privacy, choosing AI platforms, and maintenance and operations.

Developing and Deploying AI Applications Benefits

• In this course, you will:

  • Develop and Deploy AI applications and agents tailored to your organization.
  • Integrate in-house data in real-time with AI applications.
  • Secure AI applications using Security and Responsible AI by Design.
  • Maintain AI applications – performance, security, and cost/benefit.
  • Manage AI technology, platforms, threats, and risks.

• Prerequisites

  Exposure to AI at the level of Course 4700, Introduction to Artificial Intelligence (AI).

Developing AI Applications Course Outline

Learning Objectives

Chapter 1: Exploring the AI Applications and Agents Landscape

• AI for text (NLP), speech and vision; and multi-modal
• Current API Platforms and Tools
• Applications and Opportunities for AI – from pretrained to custom
• In-house AI vs Cloud and Edge AI
• Demo/Exercises  - for your workplace; identify opportunities
• How AI works – LLMs, Context Embedding, Image generation; limitations, training sets
• GenAI - Strengths, Weaknesses, Threats and Opportunities (“Hallucination”)
• Case Study/demo – PamperedPets

Chapter 2: Pretrained AI

• Why pretrained AI
• Spectrums of solutions and products: NLP (text), vision, and speech recognition
• Integration with applications
• Exercise/demo: “off the shelf” – upload an image of your government id or documents

Chapter 3: Integrating AI libraries and Frameworks

• The landscape of AI Libraries and Frameworks
• Use cases and libraries for vision, speech and text
• Accessing corporate data sources
• Exercise/Demo: Prompt Engineering
• Incorporating organizational data – backend databases, chat histories, unstructured data
• Exercise: building a chatbot that accesses organizational data

Chapter 4: Deploying GenAI in your organization

• Managing costs and risks of GenAI
• Monitoring
• Managing security
• Choosing a GenAI platform
• Exercise: deploying a chatbot
• When to use GenAI vs ML/Classification
• Exercise: planning a GenAI pilot

Chapter 5: Future of GenAI

• Rapid evolution
• Emerging trends – multi-cloud, avoiding “lockin”, avoiding “custom code”
• Integration of GenAI with Data Warehouses