Innovating with Google Cloud Artificial Intelligence

The course is a three-day program designed to introduce software developers to the world of AI-enhanced development. It centers around AI assistants, especially GitHub Copilot, and integrating AI into software applications. Participants will engage in hands-on exercises that demonstrate AI's potential in enhancing ideation, proof-of-concept creation, coding, software robustness, and debugging. The course further explores advanced prompt engineering and a tour of the Copilot ecosystem. It also provides a comparative study of different Large Language Models (LLMs), equipping participants with the knowledge to choose the most suitable model for their needs. An understanding of various cloud providers and their AI offerings is explored, enabling informed decisions about tool and platform selection. Incorporating LLMs into an application forms a significant part of the learning, along with an overview of AI security. It ensures that developers understand the AI threat landscape and are equipped with strategies to mitigate potential risks, making this course a comprehensive guide to integrating AI into both the product and the processes.

The evolving world of artificial intelligence (AI) brings both opportunities and risks. To protect assets, organizations must understand how to secure their AI systems. This in-depth course delves into the AI security landscape, addressing vulnerabilities like prompt injection, denial of service attacks, model theft, and more. Learn how attackers exploit these weaknesses and gain hands-on experience with proven defense strategies and security APIs. Discover how to securely integrate LLMs into your applications, safeguard training data, build robust AI infrastructure, and ensure effective human-AI interaction. By the end of this course, you'll be equipped to protect your organization's AI assets and maintain the integrity of your systems.

What you'll learn ✓ Apply your skills to recognize real-world generative AI uses and identify generative AI models and tools for text, code, image, audio, and video. ✓ Explain generative AI prompt engineering concepts, examples, and common tools and learn techniques needed to create effective, impactful prompts. ✓ Implement data engineering processes such as data warehouse schema design, data generation, augmentation and anonymization using generative AI tools ✓ Evaluate real-world case studies showcasing the successful application of generative AI for ETL and data repositories