Author Archives: Google Developers

Achieving privacy compliance with your CI/CD: A guide for compliance teams

Posted by Fergus Hurley – Co-Founder & GM, Checks, and Evan Otero – Product Manager, Checks

In the fast-paced world of software development, Continuous Integration and Continuous Deployment (CI/CD) have become cornerstones, enabling teams to deliver high-quality software faster than ever. However, the rise of rapid innovation, increasing use of third-party libraries, and AI-generated code have accelerated vulnerabilities and risks. Therefore, addressing these issues early in the development lifecycle is essential so that teams can launch their products quickly and confidently.

The introduction of Checks privacy compliance CI/CD tooling feature represents a significant stride towards addressing these concerns, by reducing manual intervention and automating compliance and privacy standards as part of a release cycle.

In this post, we explore the meaning of CI/CD for compliance team members unfamiliar with this technology and how Checks can weave privacy and compliance protection practices into that pipeline.

What is CI/CD?

Continuous Integration (CI) and Continuous Deployment (CD) are foundational practices in modern software development. They enable development teams to increase efficiency, improve quality, and accelerate delivery.

Continuous Integration (CI) automatically integrates code changes from multiple contributors into a software project. This practice enables teams to detect problems early by running automated tests on each change before it is merged into the main branch.

Graphic showing CI/CD continuous cycle

Continuous Deployment (CD) takes automation further by automatically deploying all code changes to a testing or production environment after the build stage. This means that, in addition to automated testing, automated release processes ensure that new changes are accessible to users as quickly as possible.

Shifting issue-spotting left with CI/CD pipelines

The automation of CI/CD processes is typically called “pipelines.” CI/CD pipelines automate the steps software changes go through, from development to deployment. These steps include compiling code, running tests (unit tests, integration tests, etc.), security scans, and more. If all automated tests pass, the changes go live without human intervention in a specific environment, such as testing or production.

These pipelines are designed to catch issues as early as possible, embodying the practice known as “shifting left.” The benefits of “shifting left”, particularly when applied through CI/CD pipelines, include:

  • Improved quality and security: Automated testing in CI/CD pipelines ensures that code is rigorously tested for functional and compliance issues before it reaches production. This early detection enables teams to address vulnerabilities and errors when they are generally easier and less costly to fix.
  • Faster release cycles: By catching and addressing issues early, teams avoid the bottlenecks associated with late-stage discovery of problems. This efficiency reduces the time from development to deployment, enabling faster release cycles and more responsive delivery of features and fixes.
  • Reduced costs: Detecting issues later in the development process can be significantly more expensive to resolve, especially if they're found after deployment. Early detection through CI/CD pipelines minimizes these costs by preventing complex rollbacks and the need for emergency fixes in production environments.
  • Increased reliability and trust: Software that undergoes thorough testing before release is generally more reliable and secure. This reliability builds trust among users and stakeholders, crucial for maintaining a positive reputation and ensuring user satisfaction.

Checks brings privacy and compliance tests to your CI/CD

TChecks CI/CD tooling seamlessly integrates app compliance scanning into CI/CD pipelines via plugins for GitHub, Jenkins, and FastLane. You can also use Checks in any other CI/CD system that supports custom scripts, such as GitLab, TeamCity, Bitbucket, and more.

image showing logos of CI/CD systems that support custom scripts - FastLane, Jenkins, GitHub, Atlassian BitBucket, GitLab, Azure DevOps, and Team City

When Checks scans an app, the binary undergoes dynamic and static analysis to understand your data collection and sharing practices, including app dependencies such as SDKs, permissions, and endpoints. This data is then tested against global regulatory requirements, store policies, your custom Checks policies, and your privacy policy to find potential issues and opportunities for improvement.

Top 5 benefits of integrating Checks into your CI/CD

image showing checks report highlighting potential issues

By adding Checks as a step in your CI/CD pipeline, you can automate app and code compliance scanning as part of the development lifecycle.

The top 5 benefits of integrating Checks in your CI/CD are:

  1. Real-time, intelligent alerting: You can stay informed of new compliance issues or changes in data behavior across your product portfolio with instant notifications via email or Slack. 
  2. Understand data sharing & SDKs: Checks can help ensure secure third-party data sharing by gaining visibility into SDK integrations, permissions, and data flow analysis. By using Checks, you can be confident in your third-party dependencies before your public release. 
  3. Ensure new builds follow your company policies: Checks enables you to automate data governance with custom policies that let you set up safeguards against specific endpoints, SDKs, data types, and permissions, tailoring privacy to your specific needs. These policies help ensure all new releases comply with your company’s data policies. 
  4. Keep your Google Play Data safety section up-to-date: Checks can recommend Google Play Data safety section disclosures and alert you if you should make an update before releasing publicly, ensuring your declarations are always up-to-date. 
  5. Deploy quickly and with confidence: When Checks finds issues in the CI/CD, these vulnerabilities are caught and remedied early, significantly reducing the risk of compliance violations once you deploy the app. Checks helps you maintain high compliance standards without slowing down the release cycle, enabling teams to deploy with confidence and ensuring that user data is protected from the outset.

Next steps

Getting started is simple. Start by first signing up for Checks and then adding Checks to your CI/CD pipelines with these simple configuration steps. Once configured, Checks is ready to perform a variety of privacy and compliance verifications.

This proactive approach to privacy and compliance safeguards against potential risks and aligns with regulatory compliance requirements, making it an invaluable asset for any compliance and development team.

Gemini 1.5 Pro Now Available in 180+ Countries; With Native Audio Understanding, System Instructions, JSON Mode and More

Posted by Jaclyn Konzelmann and Megan Li - Google Labs

Grab an API key in Google AI Studio, and get started with the Gemini API Cookbook

Less than two months ago, we made our next-generation Gemini 1.5 Pro model available in Google AI Studio for developers to try out. We’ve been amazed by what the community has been able to debug, create and learn using our groundbreaking 1 million context window.

Today, we’re making Gemini 1.5 Pro available in 180+ countries via the Gemini API in public preview, with a first-ever native audio (speech) understanding capability and a new File API to make it easy to handle files. We’re also launching new features like system instructions and JSON mode to give developers more control over the model’s output. Lastly, we’re releasing our next generation text embedding model that outperforms comparable models. Go to Google AI Studio to create or access your API key, and start building.

Unlock new use cases with audio and video modalities

We’re expanding the input modalities for Gemini 1.5 Pro to include audio (speech) understanding in both the Gemini API and Google AI Studio. Additionally, Gemini 1.5 Pro is now able to reason across both image (frames) and audio (speech) for videos uploaded in Google AI Studio, and we look forward to adding API support for this soon.

screen grab of a clooege professor using Gemini 1.5 Pro to create a quiz based on their latest lecture video in Google AI Studio
You can upload a recording of a lecture, like this 117,000+ token lecture from Jeff Dean, and Gemini 1.5 Pro can turn it into a quiz with an answer key. Video sped up for demo purposes.

Gemini API Improvements

Today, we’re addressing a number of top developer requests:

1. System instructions: Guide the model’s responses with system instructions, now available in Google AI Studio and the Gemini API. Define roles, formats, goals, and rules to steer the model's behavior for your specific use case.

image showing where System Instructions is located in Google AI Studio
Set System Instructions easily in Google AI Studio

2. JSON mode: Instruct the model to only output JSON objects. This mode enables structured data extraction from text or images. You can get started with cURL, and Python SDK support is coming soon.

3. Improvements to function calling: You can now select modes to limit the model’s outputs, improving reliability. Choose text, function call, or just the function itself.

A new embedding model with improved performance

Starting today, developers will be able to access our next generation text embedding model via the Gemini API. The new model, text-embedding-004, (text-embedding-preview-0409 in Vertex AI), achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the MTEB benchmarks.

table showing Gecko: Versativel Text Embeddings Distilled from Large Language Models
'Text-embedding-004' (aka Gecko) using 256 dims output outperforms all larger 768 dim output models on MTEB benchmarks

These are just the first of many improvements coming to the Gemini API and Google AI Studio in the next few weeks. We’re continuing to work on making Google AI Studio and the Gemini API the easiest way to build with Gemini. Get started today in Google AI Studio with Gemini 1.5 Pro, explore code examples and quickstarts in our new Gemini API Cookbook, and join our community channel on Discord.

Meet the inaugural cohort of our Google for Startups Accelerator: AI First North America

Posted by Matt Ridenour, Head of Startup Developer Ecosystem - USA

Startups are at the forefront of developing solutions for some of humanity's most pressing challenges by using AI, driving breakthroughs across industries from healthcare to cybersecurity.

To help AI-focused startups scale quickly while building responsibly, we’re thrilled to introduce the inaugural class of the Google for Startups Accelerator: AI-First program in North America. This new program is for startups building AI solutions based in the U.S. and Canada. This is the first of several AI-focused programs we'll offer throughout the year in Europe, India and Brazil.

This equity-free program provides 10 weeks of hands-on mentorship and technical project support to startups using AI in their core service or product. Selected startups will collaborate with a cohort of top peer founders and engage with leaders across Google. The curriculum will give founders access to the latest AI tools (including Google’s own Gemini), and will also include workshops on tech and infrastructure, UX and product, growth, sales, leadership and OKRs.

Meet the inaugural class of Google for Startups Accelerator: AI-First, North America

We’re thrilled to introduce the 15 AI startups selected for this accelerator:

Aptori, San Jose, CA. Aptori assists developers and security engineers to build secure, high-quality software.

Augmend, Seattle, WA. Augmend is an AI native Loom made for developers, making it possible to share expertise, not just videos.

Backpack Healthcare, Elkridge, MA. Backpack Healthcare is a pediatric mental health company utilizing proprietary AI technology, an engagement platform, and live therapists to offer personalized care to patients.

BrainLogic AI, Menlo Park, CA. BrainLogic AI has built a localized AI agent that connects users and businesses through whatsapp.

Cicerai, The Woodlands, TX. Cicerai is an AI-native Legal Practice Management Platform, boosting productivity and enhancing quality.

CLIKA, San Jose, CA. CLIKA simplifies deploying AI models on diverse hardware by offering automated model compression and format compilation.

Easel AI, Inc., Los Angeles, CA. Easel AI is an AI avatar-based social chat app that runs on iMessage.

Findly, San Francisco, CA. Findly is a data visualization integrator using a natural language chat interface.

Glass Health, San Francisco, CA. Glass Health empowers clinicians with the best-in-class AI platform for clinical decision support.

Kodif, Sunnyvale, CA. Kodif is a low-code AI-powered automation platform for support agent workflows to resolve customer issues.

Liminal, Indianapolis, IN. Liminal empowers regulated enterprises to securely deploy and use generative AI, horizontally covering every interaction and use case.

Mbue, Austin, TX. Mbue leverages AI to instantly review architectural drawings, catching errors earlier and streamlining the process.

Modulo Bio, San Diego, CA. Modulo Bio is building a platform to discover therapeutics that prevent or reverse neurodegenerative diseases.

Rocket Doctor, Toronto, ON, Canada. Rocket Doctor is a digital health platform and marketplace that intelligently matches patients and clinicians in a telemedicine 2.0 approach.

Sibli, Montreal, QC, Canada. Sibli is a fintech platform that processes unstructured data and identifies key insights for financial analysts.

The program kicks off at Cloud Next 2024 and culminates with a high profile Demo Day in June for potential partners, customers and investors.

After graduation, startups join the dynamic Google for Startups accelerator community, where they receive ongoing support and have the opportunity to build lasting connections with like-minded founders, mentors and investors.

We are honored to partner with this cohort of companies through this accelerator and beyond, to advance their AI technologies. Register your interest to get updates on the program, and join us in celebrating these exceptional startups!

Gemma Family Expands with Models Tailored for Developers and Researchers

Posted by Tris Warkentin – Director, Product Management and Jane Fine - Senior Product Manager

In February we announced Gemma, our family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. The community's incredible response – including impressive fine-tuned variants, Kaggle notebooks, integration into tools and services, recipes for RAG using databases like MongoDB, and lots more – has been truly inspiring.

Today, we're excited to announce our first round of additions to the Gemma family, expanding the possibilities for ML developers to innovate responsibly: CodeGemma for code completion and generation tasks as well as instruction following, and RecurrentGemma, an efficiency-optimized architecture for research experimentation. Plus, we're sharing some updates to Gemma and our terms aimed at improvements based on invaluable feedback we've heard from the community and our partners.

Introducing the first two Gemma variants

CodeGemma: Code completion, generation, and chat for developers and businesses

Harnessing the foundation of our Gemma models, CodeGemma brings powerful yet lightweight coding capabilities to the community. CodeGemma models are available as a 7B pretrained variant that specializes in code completion and code generation tasks, a 7B instruction-tuned variant for code chat and instruction-following, and a 2B pretrained variant for fast code completion that fits on your local computer. CodeGemma models have several advantages:

  • Intelligent code completion and generation: Complete lines, functions, and even generate entire blocks of code – whether you're working locally or leveraging cloud resources. 
  • Enhanced accuracy: Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, helping reduce errors and debugging time. 
  • Multi-language proficiency: Your invaluable coding assistant for Python, JavaScript, Java, and other popular languages. 
  • Streamlined workflows: Integrate a CodeGemma model into your development environment to write less boilerplate, and focus on interesting and differentiated code that matters – faster.
image of streamlined workflows within an exisitng AI dev project with CodeGemma integrated
This table compares the performance of CodeGemma with other similar models on both single and multi-line code completion tasks. Learn more in the technical report.

Learn more about CodeGemma in our report or try it in this quickstart guide.

RecurrentGemma: Efficient, faster inference at higher batch sizes for researchers

RecurrentGemma is a technically distinct model that leverages recurrent neural networks and local attention to improve memory efficiency. While achieving similar benchmark score performance to the Gemma 2B model, RecurrentGemma's unique architecture results in several advantages:

  • Reduced memory usage: Lower memory requirements allow for the generation of longer samples on devices with limited memory, such as single GPUs or CPUs. 
  • Higher throughput: Because of its reduced memory usage, RecurrentGemma can perform inference at significantly higher batch sizes, thus generating substantially more tokens per second (especially when generating long sequences). 
  • Research innovation: RecurrentGemma showcases a non-transformer model that achieves high performance, highlighting advancements in deep learning research. 
graph showing maximum thoughput when sampling from a prompt of 2k tokens on TPUv5e
This chart reveals how RecurrentGemma maintains its sampling speed regardless of sequence length, while Transformer-based models like Gemma slow down as sequences get longer.

To understand the underlying technology, check out our paper. For practical exploration, try the notebook, which demonstrates how to finetune the model.

Built upon Gemma foundations, expanding capabilities

Guided by the same principles of the original Gemma models, the new model variants offer:

  • Open availability: Encourages innovation and collaboration with its availability to everyone and flexible terms of use. 
  • High-performance and efficient capabilities: Advances the capabilities of open models with code-specific domain expertise and optimized design for exceptionally fast completion and generation. 
  • Responsible design: Our commitment to responsible AI helps ensure the models deliver safe and reliable results. 
  • Flexibility for diverse software and hardware:  
    • Both CodeGemma and RecurrentGemma: Built with JAX and compatible with JAX, PyTorch, , Hugging Face Transformers, and Gemma.cpp. Enable local experimentation and cost-effective deployment across various hardware, including laptops, desktops, NVIDIA GPUs, and Google Cloud TPUs.  
    • CodeGemma: Additionally compatible with Keras, NVIDIA NeMo, TensorRT-LLM, Optimum-NVIDIA, MediaPipe, and availability on Vertex AI. 
    • RecurrentGemma: Support for all the aforementioned products will be available in the coming weeks.

Gemma 1.1 update

Alongside the new model variants, we're releasing Gemma 1.1, which includes performance improvements. Additionally, we've listened to developer feedback, fixed bugs, and updated our terms to provide more flexibility.

Get started today

These first Gemma model variants are available in various places worldwide, starting today on Kaggle, Hugging Face, and Vertex AI Model Garden. Here's how to get started:

We invite you to try the CodeGemma and RecurrentGemma models and share your feedback on Kaggle. Together, let's shape the future of AI-powered content creation and understanding.

ML Olympiad 2024: Globally Distributed ML Competitions by Google ML Community

Posted by Bitnoori Keum – DevRel Community Manager

The ML Olympiad consists of Kaggle Community Competitions organized by ML GDE, TFUG, and other ML communities, aiming to provide developers with opportunities to learn and practice machine learning. Following successful rounds in 2022 and 2023, the third round has now launched with support from Google for Developers for each competition host. Over the last two rounds, 605 teams participated in 32 competitions, generating 105 discussions and 170 notebooks. We encourage you to join this round to gain hands-on experience with machine learning and tackle real-world challenges.

ML Olympiad Community Competitions

Over 20 ML Olympiad community competitions are currently open. Visit the ML Olympiad page to participate.

Smoking Detection in Patients

Predict smoking status with bio-signal ML models
Host: Rishiraj Acharya (AI/ML GDE) / TFUG Kolkata

TurtleVision Challenge

Develop a classification model to distinguish between jellyfish and plastic pollution in ocean imagery
Host: Anas Lahdhiri / MLAct

Detect hallucinations in LLMs

Detect which answers provided by a Mistral 7B instruct model are most likely hallucinations
Host: Luca Massaron (AI/ML GDE)


Find ML solutions to reduce food wastage
Host: Anushka Raj / TFUG Hajipur

Predicting Wellness

Predict the percentage of body fat in men using multiple regression methods
Host: Ankit Kumar Verma / TFUG Prayagraj

Offbeats Edition

Build a regression model to predict the age of the crab
Host: Ayush Morbar / Offbeats Byte Labs

Nashik Weather

Predict the condition of weather in Nashik, India
Host: TFUG Nashik

Predicting Earthquake Damage

Predict the level of damage to buildings caused by earthquake based on aspects of building location and construction
Host: Usha Rengaraju

Forecasting Bangladesh's Weather

Predict the rainy day; amount of rainfall, and average temperature for a particular day.
Host: TFUG Bangladesh (Dhaka)

CO2 Emissions Prediction Challenge

Predict CO2 emissions per capita for 2030 using global development indicators
Host: Md Shahriar Azad Evan, Shuvro Pal / TFUG North Bengal

AI & ML Malaysia

Predict loan approval status
Host: Kuan Hoong (AI/ML GDE) / Artificial Intelligence & Machine Learning Malaysia User Group

Sustainable Urban Living

Predict the habitability score of properties
Host: Ashwin Raj / BeyondML

Toxic Language (PTBR) Detection

(in local language)
Classify Brazilian Portuguese tweets in one of the two classes: toxics or non toxics.
Host: Mikaeri Ohana, Pedro Gengo, Vinicius F. Caridá (AI/ML GDE)

Improving disaster response

Predict the humanitarian aid contributions as a response to disasters occurs in the world
Host: Yara Armel Desire / TFUG Abidjan

Urban Traffic Density

Develop predictive models to estimate the traffic density in urban areas
Host: Kartikey Rawat / TFUG Durg

Know Your Customer Opinion

Classify each customer opinion into several Likert scale
Host: TFUG Surabaya

Forecasting India's Weather

Predict the temperature of the particular month
Host: Mohammed Moinuddin / TFUG Hyderabad

Classification Champ

Develop classification models to predict tumor malignancy
Host: TFUG Bhopal

AI-Powered Job Description Generator

Build a system that employs Generative AI and a chatbot interface to automatically generate job descriptions
Host: Akaash Tripathi / TFUG Ghaziabad

Machine Translation French-Wolof

Develop robust algorithms or models capable of accurately translating French sentences into Wolof.
Host: GalsenAI

Water Mapping using Satellite Imagery

Water mapping using satellite imagery and deep learning for dam drought detection
Host: Taha Bouhsine / ML Nomads

Navigating ML Olympiad

To see all the community competitions around the ML Olympiad, search "ML Olympiad" on Kaggle and look for further related posts on social media using #MLOlympiad. Browse through the available competitions and participate in those that interest you!

#WeArePlay | Meet the founders changing women’s lives: Women’s History Month Stories

Posted by Leticia Lago – Developer Marketing

In celebration of Women’s History month, we’re celebrating the founders behind groundbreaking apps and games from around the world - made by women or for women. Let's discover four of my favorites in this latest batch of nine #WeArePlay stories.

Múkami Kinoti Kimotho

Royelles Revolution / Royelles Revolution: Gaming For Girls (USA)

Múkami Kinoti Kimotho – Royelles Revolution / Royelles- Gaming For Girls | USA

Múkami's journey began when she noticed the lack of representation for girls in the gaming industry. Determined to change this narrative, she created Royelles, a game designed to inspire girls and non-binary people to pursue careers in STEAM (science, technology, engineering, art, math) fields. The game is anchored in fierce female avatars like the real life NASA scientist Mara who voices a character. Royelles is revolutionizing the gaming landscape and empowering the next generation of innovators. Múkami's excited to release more gamified stories and learning modules, and a range of extended reality and AI-powered avatars based on the game’s characters.

"If we're going to effectively educate Gen Z and Gen Alpha, we have to meet them in the metaverse and leverage gamified play as a means of driving education, awareness, inspiration and empowerment.” 

- Múkami

Leonika Sari Njoto Boedioetomo

Reblood: Blood Services App (Indonesia)

Leonika Sari Njoto Boedioetomo – Reblood / Blood Services App | Indonesia

When her university friend needed an urgent blood transfusion but discovered there was none available in the blood bank, Leonika became aware of the blood donation shortage in Indonesia. Her mission to address this led her to create Reblood, an app connecting blood donors with those in need. With over 140,000 blood donations facilitated to date, Reblood is not only saving lives but also promoting healthier lifestyles with a recently added feature that allows people to find the most affordable medical checkups.

“Our goal is to save more lives by raising awareness of blood donation in Indonesia and promoting healthier lifestyles for blood donors.” 

- Leonika

Luciane Antunes dos Santos and Renato Hélio Rauber

CARSUL / Car Sul: Urban Mobility App (Brazil)

Luciane Antunes dos Santos and Renato Hélio Rauber – Car Sul: Urban Mobility App | Brazil

Luciane was devastated when she lost her son in a car accident. Her and her husband Renato's loss led them to develop Carsul, an urban mobility app prioritizing safety and security. By providing safe transportation options and partnering with government health programs to chauffeur patients long distances to larger hospitals, Carsul is not only preventing accidents but also saving lives. Luciane and Renato's dedication to protecting others from the pain they've experienced is ongoing and they plan to expand to more cities in Brazil.

“Carsul was born from this story of loss, inspiring me to protect other lives. Redefining myself in this way is very rewarding.” 

- Luciane

Diariata (Diata) N'Diaye

Resonantes / App-Elles: Safety App for Women (France)

Diariata (Diata) N'Diaye – Resonantes /App-Elles: Safety App for Women | France

After hearing the stories of young people who had experienced abuse that was similar to her own, Spoken word artist Diata developed App-Elles – an app that allows women to send alerts when they're in danger. By connecting users with support networks and professional services, App-Elles is empowering women to reclaim their safety and seek help when needed.Diata also runs writing and recording workshops to help victims overcome their experiences with violence and has plans to expand her app with the introduction of a discreet wearable that sends out alerts.

“I realized from my work on the ground that there were victims of violence who needed help and support systems. This was my inspiration to create App-Elles." 

- Diata

Discover more #WeArePlay stories and share your favorites.

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Build with Google AI video series, Season 2: more AI patterns

Posted by Joe Fernandez – Google AI Developer Relations

We are off to another exciting year in Artificial Intelligence (AI) and it's time to build more applications with Google AI technology! The Build with Google AI video series is for developers looking to build helpful and practical applications with AI. We focus on useful code projects you can implement and extend in an afternoon to bring the power of artificial intelligence into your workflow or organization. Our first season received over 100,000 views in six weeks! We are glad to see that so many of you liked the series, and we are excited to bring you even more Google AI application projects.

Today, we are launching Season 2 of the Build with Google AI series, featuring projects built with Google's Gemini API technology. The launch of Gemini and the Gemini API has brought developers even more advanced AI capabilities, including advanced reasoning, content generation, information synthesis, and image interpretation. Our goal with this season is to help you put those capabilities to work for you and your organizations.

AI app patterns

The Build with Google AI series features practical application code projects created for you to use and customize. However, we know that you are the best judge of what you or your organization needs to solve day-to-day problems and get work done. That's why each application we feature in this series is also meant to be used as an AI pattern. You can extend the applications immediately to solve problems and provide value for your business, and these applications show you a general coding pattern for getting value out of AI technology.

For this second season of this series, we show how you can leverage Google's Gemini AI model capabilities for applications. Here's what's coming up:

  • AI Slides Reviewer with Google Workspace (3/20) - Image interpretation is one of the Gemini model's biggest new features. We show you how to make practical use of it with a presentation review app for Google Slides that you can customize with your organization's guidelines and recommendations. 
  • AI Flutter Code Agent with Gemini API (3/27) - Code generation was the most popular episode from last season, so we are digging deeper into this topic. Build a code generation extension to write Flutter code and explore user interface designs and looks with just a few words of description.
  • AI Data Agent with Google Cloud (4/3) - Why write code to extract data when you can just ask for it? Build a web application that uses Gemini API's Function Calling feature to translate questions into code calls and data into plain language answers.

Season 1 upgraded to Gemini API: We've upgraded Season 1 tutorials and code projects to use the Gemini API so you can take advantage of the latest in generative AI technology from Google. Check them out!

Learn from the developers

Just like last season, we'll go back to the studio to talk with coders who built these projects so they can share what they learned along the way. How do you make the Gemini model review an entire presentation? What's the most effective way to generate code with AI? How do you get a database to answer questions with the Gemini API? Get insights into coding with AI to jump start your own development project.

New home for AI developer content

Developers interested in Google's AI offerings now have a new home at There you'll find a wealth of resources for building with AI from Google, including the Build with Google AI tutorials. Stay tuned for much more content through the rest of the year.

We are excited to bring you the second season of Build with Google AIcheck out Season 2 right now! Use those video comments to let us know what you think and tell us what you'd like to see in future episodes.

Keep learning! Keep building!

Tune Gemini Pro in Google AI Studio or with the Gemini API

Posted by Cher Hu, Product Manager and Saravanan Ganesh, Software Engineer for Gemini API

The following post was originally published in October 2023. Today, we've updated the post to share how you can easily tune Gemini models in Google AI Studio or with the Gemini API.

Last year, we launched Gemini 1.0 Pro, our mid-sized multimodal model optimized for scaling across a wide range of tasks. And with 1.5 Pro this year, we demonstrated the possibilities of what large language models can do with an experimental 1M context window. Now, to quickly and easily customize the generally available Gemini 1.0 Pro model (text) for your specific needs, we’ve added Gemini Tuning to Google AI Studio and the Gemini API.

What is tuning?

Developers often require higher quality output for custom use cases than what can be achieved through few-shot prompting. Tuning improves on this technique by further training the base model on many more task-specific examples—so many that they can’t all fit in the prompt.

Fine-tuning vs. Parameter Efficient Tuning

You may have heard about classic “fine-tuning” of models. This is where a pre-trained model is adapted to a particular task by training it on a smaller set of task-specific labeled data. But with today’s LLMs and their huge number of parameters, fine-tuning is complex: it requires machine learning expertise, lots of data, and lots of compute.

Tuning in Google AI Studio uses a technique called Parameter Efficient Tuning (PET) to produce higher-quality customized models with lower latency compared to few-shot prompting and without the additional costs and complexity of traditional fine-tuning. In addition, PET produces high quality models with as little as a few hundred data points, reducing the burden of data collection for the developer.

Why tuning?

Tuning enables you to customize Gemini models with your own data to perform better for niche tasks while also reducing the context size of prompts and latency of the response. Developers can use tuning for a variety of use cases including but not limited to:

  • Classification: Run natural language tasks like classifying your data into predefined categories, without needing tons of manual work or tools.
  • Information extraction: Extract structured information from unstructured data sources to support downstream tasks within your product.
  • Structured output generation: Generate structured data, such as tables, quickly and easily.
  • Critique Models: Use tuning to create critique models to evaluate output from other models.

Get started quickly with Google AI Studio

1. Create a tuned model

It’s easy to tune models in Google AI Studio. This removes any need for engineering expertise to build custom models. Start by selecting “New tuned model” in the menu bar on the left.

moving image showing how to create a tuned model in Google AI Studio by opening 'New Tuned Model' from the menu

2. Select data for tuning

You can tune your model from an existing structured prompt or import data from Google Sheets or a CSV file. You can get started with as few as 20 examples and to get the best performance, we recommend providing a dataset of at least 100 examples.

moving image showing how to select data for tuning in Google AI Studio by importing data

3. View your tuned model

View your tuning progress in your library. Once the model has finished tuning, you can view the details by clicking on your model. Start running your tuned model through a structured or freeform prompt.

moving image showing how to view your tuned model in Google AI Studio by importing data

4. Run your tuned model anytime

You can also access your newly tuned model by creating a new structured or freeform prompt and selecting your tuned model from the list of available models.

moving image demonstrating what it looks like to run your tuned model in Google AI Studio after importing data

Tuning with the Gemini API

Google AI Studio is the fastest and easiest way to start tuning Gemini models. You can also access the feature via the Gemini API by passing the training data in the API request when creating a tuned model. Learn more about how to get started here.

We’re excited about the possibilities that tuning opens up for developers and can’t wait to see what you build with the feature. If you’ve got some ideas or use cases brewing, share them with us on X (formerly known as Twitter) or Linkedin.

Tune in for Google I/O on May 14

Posted by Jeanine Banks – VP & General Manager, Developer X, and Head of Developer Relations

Google I/O is arriving this year on May 14th and you’re invited to join us online! I/O offers something for everyone, whether you are developing a new application, modernizing an existing one, or transforming it into a business.

The Gemini era unlocks new possibilities for developers to build creative and productive AI-enabled applications. I/O is where you’ll hear how you can get from idea to production AI applications faster. We’re excited to share what’s new for mobile, web, and multiplatform development, and how to scale your applications in the cloud. You will be able to dive deeper into topics that interest you with over 100 sessions, workshops, codelabs, and demos.

Visit the Google I/O site and register to stay informed about I/O and other related events coming soon. The livestreamed keynotes start May 14 at 10am PT, so mark your calendar.

If you haven’t already, go try out our newest Google I/O puzzle and head to @googlefordevs on Instagram if you need a hint.

GDE Women’s History Month Feature: Gema Parreño Piqueras, AI/ML GDE

Posted by Justyna Politanska-Pyszko – Program Manager, Google Developer Experts

For Women's History Month, we're shining a spotlight on Gema Parreño Piqueras, an AI/ML Google Developer Expert (GDE) from Madrid, Spain. GDEs are recognized by Google for their outstanding technical expertise and passion for sharing knowledge.
Gema Parreño Piqueras, AI/ML GDE, Madrid, Spain
Gema Parreño Piqueras, AI/ML GDE, Madrid, Spain

Gema's dedication to the GDE program makes her a true leader within the Google Developers community, and her work in Artificial Intelligence and Machine Learning pushes the boundaries of Google's technological capabilities.

Gema is a force to be reckoned with in the world of data science. As a data scientist at Izertis and a GDE, she's not only making significant contributions to the field of AI/ML but also blazing a trail for women in tech. Her unique background in architecture and her passion for problem-solving led her to an impressive career in AI/ML and development of her extraordinary project – helping NASA track asteroids! Learn more about her projects incorporating AI:

NASA Project: Deep Asteroid

Gema's architectural skills proved invaluable when she turned her attention to AI. In 2016, she created the program Deep Asteroid for NASA's International Space Apps Challenge. This innovative program assists scientists in detecting, tracking, and classifying asteroids, potentially protecting our planet from future threats.

Journey to AI/ML

Intrigued by the potential of AI, Gema embarked on a journey that merged her architectural background with cutting-edge technology. Her experience with 3D modeling translated seamlessly into the world of machine learning, giving her a fresh perspective. Over the past seven years, she's overcome challenges and established herself as a true expert.

As a Google Developer Expert, Gema has found a vibrant community that has fueled her growth. She has attended numerous GDE events throughout Europe and had the opportunity to collaborate with Google teams. This experience was instrumental in the development of Deep Asteroid, demonstrating the power of community and access to advanced technology.

Gema’s advice for women aspiring to enter the field is simple and powerful: "Don't be afraid to experiment, fail, and learn from those failures. Persistence and a willingness to dive into the unknown are what will set you apart." Gema encourages women to find supportive communities, like the GDE program, where they can network, learn, and grow.

You can find Gema on LinkedIn, GitHub and X (formerly known as twitter).

The Google Developer Experts (GDE) program is a global network of highly experienced technology experts, influencers, and thought leaders who actively support developers, companies, and tech communities by speaking at events and publishing content.