Podcast Summary: Software Engineering Daily - AI Developer Tools at Google with Paige Bailey

Release Date: January 9, 2025

In this engaging episode of Software Engineering Daily, host Jordymon Companies sits down with Paige Bailey, the Uber Technical Lead of the Developer Relations team at Google ML Developer Tools. They delve deep into Google's suite of machine learning (ML) and artificial intelligence (AI) developer tools, exploring their evolution, functionalities, and future prospects.

1. Introduction and Speaker Background

[00:00 - 02:07]

The episode kicks off with Jordymon Companies introducing Paige Bailey and her role at Google. Paige oversees a range of ML developer tools, including Gemini APIs, Gemma, AI Studio, Kaggle, Colab, and Jax. Her extensive experience in the ML field, starting around 2009, positions her as a seasoned expert in developing and managing AI tools for developers.

Notable Quote:

Paige Bailey [00:54]: "I am so excited to be here and really excited to have the opportunity to talk to you and also loved the questions that you are asking before we hit record. I think this is going to be a fun conversation."

2. Evolution of AI and Multimodal Models

[02:07 - 04:47]

Paige discusses the transformative journey of AI, emphasizing the shift from single-task models to multimodal models that handle text, code, video, audio, and images. She highlights how modern transformer models like GPT-3 have expanded capabilities, enabling more integrated and versatile applications.

Notable Quote:

Paige Bailey [03:09]: "We’re getting into this really brave new world of multimodal models. So not just this underpinning language backbone, but also really interesting capabilities in terms of video understanding, audio understanding and transcription image understanding kind of coupled with text and code as well."

3. Google's AI Developer Tools: Gemini API and Gemma

[04:47 - 17:25]

Paige provides an overview of Google's AI developer ecosystem. She distinguishes between Gemini APIs, which are proprietary and accessible via REST APIs, and Gemma, an open-source family of models available for customization and local deployment. Gemini APIs offer high performance and are designed for scalability, while Gemma caters to developers needing flexibility and control over their models.

Notable Quote:

Paige Bailey [17:25]: "One of the nice things about open source models is that if you're running them locally, that's kind of free, you're just using your onboard compute. You might want to customize in ways that you would not be able to with a proprietary model..."

4. Target Personas and Use Cases

[08:29 - 11:57]

The discussion moves to the different user personas for Google's AI tools:

• Jax Users: Experts building large-scale models, similar to those used by DeepMind.
• Gemma Users: Developers fine-tuning models for specific applications or deploying them on various platforms.
• Gemini API Users: A broader audience utilizing REST APIs for easier integration without deep ML expertise.

Paige emphasizes the versatility of Gemini APIs in simplifying the ML Ops process, making advanced AI accessible to a wider range of developers.

Notable Quote:

Paige Bailey [09:06]: "The beautiful thing about the Gemini APIs is that if you can make a REST API call, then you can call the Gemini model."

5. Kaggle Workshop on Generative AI

[11:57 - 13:48]

Paige recounts a recent five-day generative AI intensive course hosted on Kaggle, Google's platform for data science competitions and learning. The course attracted approximately 150,000 students, offering hands-on experience with prompting models, retrieval embeddings, fine-tuning, and implementing evaluations. The curriculum was designed to cater to a diverse audience, from beginners to advanced researchers.

Notable Quote:

Paige Bailey [12:26]: "We recently did a five day generative AI intensive course on Kaggle... But the curriculum we designed was focused not just on the model calls, but also on all of the additional features that you need to have around the models in order to make these systems production ready."

6. AI Studio: Features and Capabilities

[23:37 - 25:00]

AI Studio is introduced as Google's interactive platform for experimenting with Gemini models. It allows users to:

• Experiment with various Gemini models.
• Engage in image generation.
• Utilize features like function calling and code execution.
• Compare different models.
• Fine-tune models and generate API keys.

AI Studio aims to provide an intuitive interface, especially beneficial for junior developers navigating complex ML workflows.

Notable Quote:

Paige Bailey [23:44]: "AI Studio is a place where you can go, you can kind of experiment with the different Gemini models... all without having to kind of wrangle with the Google Cloud console."

7. Advanced Features: Code Execution and Function Calling

[26:14 - 30:17]

Paige highlights advanced functionalities within Gemini APIs:

• Code Execution: Allows models to write and execute Python code to solve tasks, enhancing problem-solving capabilities.
• Function Calling: Enables models to interact with specific tools or APIs, such as databases or weather services, to perform complex operations.

These features introduce a degree of agency to AI models, enabling them to perform iterative tasks and utilize external resources dynamically.

Notable Quote:

Paige Bailey [26:19]: "I really, really love code execution and function calling just because... it's a one liner change. All you have to do is say like tools equals code execution and you're off to the races."

8. Integration into Developer Tools and Devices

[30:17 - 32:17]

Google has integrated Gemini models into popular developer tools and devices:

• Android Studio: Enhances code completion and generation capabilities.
• Chrome Browser: Embedded within the Chrome Canary release for on-device AI processing.
• Pixel Devices: Gemini Nano models run directly on the operating system, enabling on-device AI functionalities.

These integrations aim to streamline the development process, offering AI assistance directly within the tools developers use daily.

Notable Quote:

Paige Bailey [30:25]: "Gemini models have been baked into Android Studio as well for code completion as well as code generation."

9. Retrieval Techniques and Use Cases

[19:14 - 23:37]

Paige explains retrieval techniques like RAG (Retrieval-Augmented Generation), which enhance model outputs by grounding them in specific data sources. This approach improves accuracy and reduces hallucinations by sourcing information from, for example, a company's internal documents.

Use Case Example: A CTO can feed company guidelines into the retrieval system, ensuring that junior developers receive responses aligned with the company's coding styles and policies.

Notable Quote:

Paige Bailey [19:28]: "Retrieval is really kind of doing this kind of extraction from sources that might be relevant, giving that to the model and then having the model summarize those insights as outputs."

10. Future of AI Models and Agentic Properties

[30:21 - 33:51]

The conversation touches on the agentic properties of AI models, where models can perform tasks autonomously by executing code and utilizing tools. Paige envisions a future where models like Gemini can manage schedules, perform complex queries, and execute multi-step tasks with oversight to ensure compliance and accuracy.

Notable Quote:

Paige Bailey [32:12]: "I would love to say, hey Gemini, please look on my calendar and find the next best time for me to go and like do yoga... those are all things that could be done today."

11. Resources and Upcoming Features

[33:51 - 37:49]

Paige encourages listeners to explore AI Studio and participate in educational courses on Kaggle. She also highlights upcoming features like multimodal paradigms and a new video model called VEO, which allows for video description and generation. For the latest updates, she recommends following the Google Devs Twitter handle and other team members.

Notable Quote:

Paige Bailey [37:30]: "Just the takeaway for everybody should be if you haven't tried out aistudio.google.com, go explore it like test it out on your own data."

Conclusion

Paige Bailey provides a comprehensive overview of Google's AI developer tools, emphasizing their versatility, scalability, and integration capabilities. She underscores Google's commitment to making advanced AI accessible to a broad spectrum of developers, from novices to experts, through platforms like AI Studio and Gemini APIs. The episode concludes with an encouraging note for listeners to engage with these tools and participate in upcoming educational initiatives.

Final Quote:

Paige Bailey [37:30]: "If you haven't tried out aistudio.google.com, go explore it like test it out on your own data. And you know we have a very generous free tier, so I strongly, strongly encourage you to take advantage of it."

Stay Connected:

• AI Studio: aistudio.google.com
• Google Devs Twitter: @GoogleDevs
• Kaggle Generative AI Course: Accessible through the Kaggle platform.

For more insights and updates, follow Paige Bailey and her team on various social media platforms as mentioned during the podcast.