Developing and Deploying AI Applications

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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 c ases 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). Some programming experience is highly recommended. 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 Show more