Leveraging the Gemini Pro Vision model for image understanding, multimodal prompts and accessibility

Explore how you can use the new Gemini Pro Vision model with the Gemini API to handle multimodal input data including text and image prompts to receive a text result. In this solution, you will learn how to access the Gemini API with image and text data, explore a variety of examples of prompts that can be achieved using images using Gemini Pro Vision and finally complete a codelab exploring how to use the API for a real-world problem scenario involving accessibility and basic web development.

• Gemini

Getting started with the Gemini API and Android

Learn how to use the Gemini API and the Google AI SDK to prototype generative AI in Android applications.

• Android
• Vertex AI in Firebase
• Gemini

Getting started with the Gemini API and Web apps

Learn how to use the Gemini API and the Google AI JavaScript SDK to prototype generative AI for web apps. Use the Google AI JavaScript SDK to make your first generative AI call using the Gemini API in your client-side web application. Explore a sample application and learn how to make multimodal prompts (that combine image and text).

• Vertex AI in Firebase
• Gemini

Getting started with the Gemini API and Dart and Flutter

Learn how to use the Gemini API and the Google AI Dart SDK to prototype generative AI in Dart and Flutter applications.

• Gemini
• Flutter
• Vertex AI in Firebase

Getting started with the Gemini API and Swift

Learn how to use the Gemini API and the Google AI Swift SDK to prototype generative AI with Swift. Use the Google AI Swift SDK to make your first generative AI call using the Gemini API in your application. Explore a sample application and learn how to make multimodal prompts (that combine image and text).

• Gemini
• Vertex AI in Firebase

Toy Store Search App with Cloud Databases, Serverless Runtimes and Open Source Integrations

In this codelab, you’ll build a RAG based Vector Search application designed to find matching toys for customer search (through texts and images), create custom toys based on user request and predict price for the custom-created toy using AlloyDB, Gemini, Imagen, LangChain4j and GenAI Toolbox for Databases.

Getting started with Google AI Studio and the Gemini API using Node.js

Learn how to prototype text-based prompts with Google AI Studio and get started writing your first Gemini API Node.js script.

• Gemini

Building an LLM and RAG-based chat application using AlloyDB AI and LangChain

In this codelab you’ll learn how to create an AlloyDB cluster, deploy GenAI Databases Retrieval Service for databases and create a sample application using the service.

• AlloyDB for PostgreSQL

Getting started with Vector Embeddings with AlloyDB AI

In this codelab you’ll learn how to use AlloyDB AI in combination with vector search and create an index on the vector data

• AlloyDB for PostgreSQL

Getting started with Vector Embeddings in Cloud SQL for PostgreSQL

In this codelab you’ll learn how to use Cloud SQL AI integration in combination with vector search and create an index on the vector data

• Cloud SQL

Installing and Setting-up Toolbox for your Gen AI & Agentic Applications on AlloyDB

In this codelab, you’ll build and deploy a Toolbox for your Price Prediction Application that uses AlloyDB and generative AI features using Gen AI Toolbox for Databases service.

Incorporating generative AI into your game development process with Gemini and Gemma AI

Learn how generative AI can be used in different stages of game development from preproduction to in-game solutions using Gemini AI and Gemma model.

• Gemini

Build a contextual Yoga Poses recommender app with Firestore, Vector Search and Gemini 2.0!

In this codelab, you’ll build a knowledge-driven contextual Yoga Poses search app designed to answer user questions on Yoga poses to match with their questions. It also allows administrative tasks like creating and editing Yoga poses.

Gemini in Java with Vertex AI and LangChain4j

In this codelab, you’ll chat with your users, ask questions about your documentation, or extend a model with function calling, using Generative AI in Java, integrating the Gemini large language model on Vertex AI, and leveraging the LangChain4j framework

Creating a multiplayer crossword with Gemini, Flutter, and Firebase

Learn how the Google engineering teams created a multiplayer crossword using Gemini, Flutter, and Firebase.

• Firebase
• Flutter

Build a Smart Shopping Assistant with AlloyDB and Vertex AI Agent Builder - Part 1

In this codelab, you’ll build a knowledge-driven chat application designed to answer customer questions, guide product discovery, and tailor search results for an ecommerce dataset

Access Gemini chat with python sdk via Private Service Connect endpoint

Access Gemini from a VM via python sdk and PSC endpoint

• Vertex AI

Create a Generative Chat App with Vertex AI Conversation

In this codelab, you will use Vertex AI Conversation to create, configure, and deploy a Data Store Agent and chat app to answer customer questions about products in the Google Store.

Build a custom pre-trained Audio Classification model

In this codelab, you’ll learn to customize a pre-trained Audio Classification model to detect bird sounds.

• TensorFlow
• Android

Build a computer vision model with TensorFlow

Learn to create a computer vision model that recognizes items of clothing with TensorFlow.

• TensorFlow

Say hello to the "Hello, World" of machine learning

Learn the basics of creating a

• TensorFlow

Create a simple website that classifies images

Learn how to create a simple website that classifies images with TensorFlow Serving.

• TensorFlow

Create an iOS app to predict values

Learn how to build an iOS app that detects objects within images and renders the results in its UI.

• TensorFlow

Call Vision API Product Search backend on Android

In this codelab, you’ll add code to an Android app to call a Vision API Product Search backend so that the app users can search for products using images.

• Android

Build and deploy a custom object detection model with TensorFlow Lite (Android)

In this codelab, you’ll build an Android app that can detect objects in images. You’ll start with training a custom object detection model with TFLite Model Maker and then deploy it with TFLite Task Library

• Android
• TensorFlow

Train a comment-spam detection model with TensorFlow Lite Model Maker

Learn how to train a comment-spam detection model with TensorFlow Lite Model Maker.

• Flutter
• TensorFlow

Build convolutional neural networks (CNNs) to enhance computer vision

Learn to build CNNs that make computers more efficient at classifying the contents of an image based on the detected features.

• TensorFlow

Create a custom text-classification model with TensorFlow Lite Model Maker

Learn how to retrain the spam-detection model to detect specific types of spam with TensorFlow Lite Model Maker.

• TensorFlow
• Flutter

Get started with text classification in Flutter apps

Learn about text classification and how to implement it in Flutter apps.

• TensorFlow

Go further with object detection

Learn to train your own custom object-detection models using TensorFlow Lite and the TensorFlow Lite Model Maker library, and build on all the skills you gained in the Get started with object detection pathway.

• TensorFlow
• Android

Get started with image classification in web apps

Learn about image classification and how to implement it in web apps.

• TensorFlow

Build a product image search backend with Vision API Product Search

In this codelab, you’ll learn how to build a product image search backend using Vision API Product Search, and how to create an API key to call the backend from mobile apps.

• Android

Go further with product image search

Learn how to build and call a product image search backend from a mobile app, enhancing the product search feature you built in the Get started with product image search pathway.

• TensorFlow
• Android

Create a Flutter app to classify texts

Learn how to build a Flutter app that classifies texts and displays the results in its UI.

• TensorFlow
• Flutter

TensorFlow.js: Build a comment spam detection system

In this codelab, you’ll learn how to build a simple web page that has commenting ability akin to a blog post article and integrate it with a pre trained machine learning model to detect comment spam posts, enabling you to filter these out before they even get stored in any backend database, reducing server processing time and cost.

• TensorFlow

Use convolutional neural networks (CNNs) with complex images

Learn to use CNNs with complex images in which the subject could be anywhere.

• TensorFlow

Use convolutional neural networks (CNNs) with large datasets to avoid overfitting

Learn to use CNNs with large datasets to avoid overfitting.

• TensorFlow

Get started with mobile image classification

Learn how to use computer vision to build a mobile app capable of seeing and labeling images.

• TensorFlow
• Android

Create an Android app to detect objects within images

Learn how to build an Android app that detects objects within images and renders the results in its UI.

• Android
• TensorFlow

Integrating machine learning APIs

In this codelab, we’ll explore the Vision, Speech-to-Text, Translation and Natural Language APIs. At the end, we’ll use these APIs to analyse audio recordings and map them to relevant images.

Get started with regression in iOS apps

Learn about regression and how to implement it in iOS apps.

• TensorFlow

Build your first Computer Vision App on Android or iOS

In this codelab, you’ll learn how to build a simple app for Android and iOS capable of correctly labeling an image.

• TensorFlow
• Android

Build convolutions and perform pooling

Learn to build convolutions and perform pooling to enhance computer vision.

• TensorFlow

Create a basic app for audio classification

In this codelab, you’ll learn to create a basic app for audio classification, that can record audio using the phone’s microphone.

• TensorFlow
• Android

Build a computer vision model with TensorFlow

Learn to create a computer vision model that recognizes items of clothing with TensorFlow.

• TensorFlow

TensorFlow.js: Retrain a comment spam detection model to handle edge cases

In this codelab you’ll learn how to use Model Maker to retrain a comment spam model to account for edge cases the pre-trained model could not handle, and then re-deploy the new model to the web application.

• TensorFlow

Get started with object detection in Android apps

Learn about object detection and how to implement it in Android apps.

• TensorFlow

Using the Translation API with Python

In this tutorial, you’ll learn to use the Translation API with Python.

Build a Smart Shopping Assistant with AlloyDB and Vertex AI Agent Builder - Part 2

In this codelab, you’ll build a knowledge-driven chat application designed to answer customer questions, guide product discovery, and tailor search results for an ecommerce dataset

Add on-device Text Classification to your app with TensorFlow Lite and Firebase - Android Codelab

In this codelab you’ll learn how to implement Text Classification using Firebase and TensorFlow Lite.

• Android
• TensorFlow
• Firebase
• Firebase ML

Add Recommendations to your app with TensorFlow Lite and Firebase - Android Codelab

In this codelab, you’ll learn how to create a content recommendation engine for your app using Tensorflow and Firebase.

• Firebase ML
• TensorFlow
• Google Analytics
• Android
• Firebase

Add Recommendations to your app with TensorFlow Lite and Firebase - iOS Codelab

In this codelab, you’ll learn how to create a content recommendation engine for your app using Tensorflow and Firebase

• Firebase ML
• Google Analytics
• TensorFlow
• Firebase

Add on-device Text Classification to your app with TensorFlow Lite and Firebase - iOS Codelab

In this codelab you’ll learn how to implement Text Classification using Firebase and TensorFlow Lite.

• Firebase
• TensorFlow
• Firebase ML

Build a Patent Search Assistant with AlloyDB and Vertex AI Agent Builder - Part 2

In this codelab, you’ll build a knowledge-driven chat application designed to answer questions related to patent search and bring contextually relevant results grounded in the patent dataset truth as its source.

Build apps for Google Chat with Gemini

In this codelab, you build Google Chat apps with the latest available features, which include Vertex AI’s Gemini-powered AI models, Dialogflow CX, app home, Google Chat Events, and accessory widgets.

• Google Workspace

Get started with object detection

Learn the basics of object detection and how to integrate a pretrained object detector into your mobile app.

• Android
• TensorFlow

Go further with image classification

Learn to build custom image-classification models and improve the skills you gained in the Get started with image classification pathway.

• TensorFlow
• ML Kit
• Android

Get started with product image search

Learn how to build a product image search feature for a mobile app using on-device Object Detection.

• TensorFlow
• Android

Get started with mobile text classification

Learn how to use machine learning in a mobile app to identify text and build an app capable of filtering spam from comments.

• TensorFlow
• Android

Building an LLM and RAG-based chat application using Cloud SQL databases and LangChain

In this codelab you’ll learn how to create a database, deploy GenAI Retrieval Service for databases and create a sample chat application using the service.

• Cloud SQL

How to Interact with APIs Using Function Calling in Gemini

In this codelab, you will use function calling in Gemini to build an app for users to ask about exchange rates, fetch the latest data from an external API, then respond to the user with the answer.

Building a Google-quality Search System with Vertex AI

In this codelab, you will build a Google quality search engine that can help answer queries from your documents and text files using Vertex AI Search/Agent Builder.

• Google Cloud
• Vertex AI

Deterministic generative AI with Gemini function calling in Java

Demonstrates Gemini function calling feature in a Java application by invoking the Gemini model to orchestrate the input for function calling, invoke the API, and then process the response in another Gemini call and deploy it to a REST endpoint.

Automate Google Workspace tasks with the Gemini API

Learn how to use the power of the Gemini API to automate Google Workspace tasks and explore further possibilities.

• Google Workspace

Gemini API Firebase Extensions: Quickly enhance your app with generative AI

Learn how the Gemini API Firebase Extensions enable you to add Gemini to your apps without becoming an AI expert.

• Gemini
• Firebase
• Firebase Extensions

Bookshelf builder: Use Gemini to build a Java Cloud Function for a Gemini application

We’ll create a book recommendation and summarization app using Vertex AI generative AI (Gemini) in a Cloud Function as a remote function from BigQuery..

Cloud Function that wraps the PaLM Text Bison Models

Demonstrates a Cloud Function written in Python that initializes the Vertex AI module and then provides an endpoint to invoke PaLM Text Bison model.

Bookshelf Analytics: Use Gemini to build SQL applications with BigQuery and Generative AI

We’ll use Gemini to help us create book recommendation and summarization analytics with BigQuery (SQL-only Generative AI).

Bookshelf Analytics: Use Gemini to build a Java Cloud Run Application that takes BigQuery data to the web

We’ll use Gemini to help us create a bookshelf summarization application that simply takes BigQuery data to the web and is deployed on Cloud Run.

• Cloud Run

Using the Speech-to-Text API with Python

In this tutorial, you will learn to use the Speech-to-Text API with Python.

Using the Text-to-Speech API with Python

In this tutorial, you will learn to use the Text-to-Speech API with Python.

Generative AI powered chat with users and docs in Java with PaLM and LangChain4J

In this codelab, you’ll chat with your users, or ask questions about your documentation, using Generative AI in Java, integrating the PaLM large language model, and leveraging the LangChain4J LLM orchestration framework

Similarity Search with Spanner and Vertex AI

Build a similarity search application for apparel recommendation based on user input and perform search against data stored in spanner and indexed by Vector Search to respond with the nearest neighbors.

Generative AI text generation in Java with PaLM and LangChain4J

In this codelab, you’ll get started with Generative AI in Java, integrating the PaLM large language model, and leveraging the LangChain4J LLM orchestration framework

Data to Generative AI with Spanner and Vertex AI Imagen API

Build a pose generator app to generate images based on pose prompts created by the user with data fetched from Spanner database using the server app API.

Slack Command Automation

Source code for creating a Slack Slash Command for text summarization in the Slack Application. The Slack Application uses the Cloud Function to invoke the PaLM API for text summarization.

Text Summarization Methods using Vertex AI PaLM API

In this tutorial, you will learn how to use generative models to summarize information from text by working through the stuffing method

Generative AI for Video Analytics with Vertex AI

Learn how to analyze views of influencers on Youtube on any company or product, using Google’s Gen AI capabilities.

Cloud Function for content summarization using PaLM Vertex AI API and Google Cloud Storage

Cloud Function that demonstrates how to process an uploaded file in Google Cloud Storage and perform summarization using Vertex AI PaLM API on the contents.

Chat App with PaLM API on Cloud Run

Develop a chat application by using Python Flask framework and Vertex AI PaLM API model.

• Cloud Run

Generative Insights with BigQuery SQL and Vertex AI

Build a Movie Success Rating prediction and prescription app with BigQuery SQL queries and Vertex AI PaLM API.

Text Summarizer app with Vertex AI and Svelte Kit

Build a Text Summarization use case to allow users to summarize articles, text, and other forms of content using Google Cloud Vertex AI on a Svelte Kit web app.

Recognize, Identify Language and Translate text with ML Kit and CameraX: Android

In this codelab, you'll build an Android app with ML Kit that uses on-device Machine Learning to recognize, identify language and translate text from between 59 languages. You'll also learn how to integrate the CameraX library to accomplish these tasks from a real-time camera feed.

• Firebase
• Android

Using the Natural Language API with Python

In this tutorial, you will learn to use the Natural Language API with Python.

SQL-only LLM with BigQuery ML using Vertex AI PaLM API

Source code summarization for Github repos available as a BigQuery public dataset, using Vertex AI Large Language Model for text generation (text-bison) as a hosted remote function in BigQuery.

Recognize text and facial features with ML Kit: Android

In this codelab, you’ll build an Android app with ML Kit that uses on-device Machine Learning to recognize text and facial features in images.

• ML Kit
• Android

Form Parsing with Document AI (Python)

In this codelab, you will learn how to use the Document AI Form Parser to parse a handwritten form with Python. We will use a simple medical intake form as an example, but this procedure will work with any generalized form supported by DocAI. This

Document AI Workbench - Custom Document Extractor

In this codelab, you will learn how to use Document AI Workbench to create fully customized models using your own training data.

Optical Character Recognition (OCR) with Document AI (Python)

In this codelab, you will perform Optical Character Recognition (OCR) of PDF documents using Document AI and Python. You will explore how to make both Online (Synchronous) and Batch (Asynchronous) process requests.

Managing Document AI processors with Python

In this tutorial, you will learn to manage Document AI processors with Python.

Computer Vision Made Easy: Vision AI on Spring Boot and Java

We’ll create a Computer Vision application using Spring Boot and Java, enabling you to unlock the potential of image recognition and analysis in your projects.

• Cloud Run

Movie Rating prediction with Vertex AI AutoML

We’ll create a Movie Score prediction model using Vertex AI AutoML and deploy it to an API endpoint and trigger the prediction API from Java Cloud Functions.

Using the Video Intelligence API with Python

In this tutorial, you will learn to use the Video Intelligence API with Python.

Using the Vision API with Python

In this tutorial, you will learn to use the Vision API with Python.

Specialized Processors with Document AI (Python)

In this codelab, you will learn how to use Procurement DocAI and Lending DocAI to classify and parse specialized documents.

Document AI Workbench - Uptraining

In this codelab, you will learn how to use Document AI Uptraining to improve model quality using your own training data.

Using Document AI Warehouse to Ingest, Process, and Search Documents

In this codelab, you will use Document AI Warehouse to ingest, process, and search the full text of documents.

Document AI: Human in the Loop

In this codelab, you will learn how to use Document AI Human in the Loop to complete Human Review tasks with Specialized Processors.

Movie Rating Prediction with BQML using SQL

We’ll create a Movie Score prediction model using only SQL with BigQuery ML

TensorFlow.js: Retrain a comment spam detection model to handle edge cases

In this codelab you’ll learn how to use Model Maker to retrain a comment spam model to account for edge cases the pre-trained model could not handle, and then re-deploy the new model to the web application.

• TensorFlow

TensorFlow.js — Making Predictions from 2D Data

In this codelab, you’ll train a model to make predictions from numerical data. Given the “Horsepower” of a car, the model will try to predict “Miles per Gallon” for that car. In machine learning terminology, this is described as a regression task as it predicts a continuous value.

• TensorFlow

Realtime on-device In-app-purchase optimization Codelab

In this codelab you’ll learn how to deploy a TFLite model to personalize your app for each user and optimize in-app-purchase

• Google Analytics
• TensorFlow
• Android
• Firebase A/B Testing
• Firebase Remote Config
• Firebase ML
• Firebase
• BigQuery

Vertex AI: Custom training job and prediction using managed datasets

The focus of this demo is you can use Vertex AI to train and deploy a ML model. It assumes that you are familiar with Machine Learning even though the machine learning code for training is provided to you. You will use Datasets for dataset creation

TensorFlow.js — Handwritten digit recognition with CNNs

In this codelab, you’ll train a model to identify handwritten digits. In machine learning terminology, this is known as a classification task as it predicts a category for a given input.

• TensorFlow

AI Magic Wand with TensorFlow Lite for Microcontrollers and Arduino

In this codelab, you’ll learn to build a magic wand that runs a gesture detection model using TensorFlow Lite for Microcontrollers on an Arduino. (Source)

• TensorFlow

Prototyping models in AI Platform Notebooks

In this lab you’ll learn how you can use AI Platform Notebooks for prototyping your machine learning workflows. We’ll cover creating a custom notebooks instance, tracking your notebook code in git, and debugging models with the What-If Tool.

• TensorFlow

Deploy a basic "Google Translate" Express.js app on App Engine, Cloud Functions, and Cloud Run

In this codelab, you will learn how to use the Google Cloud Translation API with Node.js and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Use Procurement Document AI to Parse your Invoices using AI Platform Notebooks

You will learn how to use Procurement DocAI to intelligently parse an invoice.

Train and deploy on-device image classification model with AutoML Vision in ML Kit

In this codelab you will train an image classifier using AutoML Vision Edge in ML Kit, and run it on an Android or iOS phone using the ML Kit SDK.

• TensorFlow
• ML Kit
• Firebase
• Android

Time Series Forecasting with Vertex AI and BigQuery ML

In this lab, you’ll learn about how to solve time-series problems using Vertex AI, covering Notebooks, Training, Prediction, and BigQuery ML.

• TensorFlow

Detect objects in images to build a visual product search with ML Kit: Android

In this codelab, you’ll build an Android app with ML Kit that uses on-device Machine Learning to detect objects in images, then lets users conduct a visual product search.

• ML Kit
• Android

Recognize, Identify Language and Translate text with ML Kit and CameraX: Android

In this codelab, you’ll build an Android app with ML Kit that uses on-device Machine Learning to recognize, identify language and translate text from between 59 languages. You’ll also learn how to integrate the CameraX library to accomplish these tasks from a real-time camera feed.

• ML Kit
• Android
• Firebase

Modern convnets, squeezenet, Xception, with Keras and TPUs

In this lab, you will learn about modern convolutional architecture and use your knowledge to implement a simple but effective convnet called “squeezenet”. This lab includes the necessary theoretical explanations about convolutional neural networks and is a good starting point for developers learning about deep learning.

• TensorFlow

TPU-speed data pipelines: tf.data.Dataset and TFRecords

TPUs are very fast. The stream of training data must keep up with their training speed. In this lab, you will learn how to load data from GCS with the tf.data.Dataset API to feed your TPU.

• TensorFlow

Convolutional neural networks, with Keras and TPUs

In this lab, you will learn how to assemble convolutional layer into a neural network model that can recognize flowers. This time, you will build the model yourself from scratch and use the power of TPU to train it in seconds and iterate on it design. This lab includes the necessary theoretical explanations about convolutional neural networks and is a good starting point for developers learning about deep learning.

• TensorFlow

Your first Keras model, with transfer learning

In this lab, you will learn how to build a Keras classifier. Instead of trying to figure out the perfect combination of neural network layers to recognize flowers, we will first use a technique called transfer learning to adapt a powerful pre-trained model to our dataset. This lab includes the necessary theoretical explanations about neural networks and is a good starting point for developers learning about deep learning.

Keras and modern convnets, on TPUs

In this lab, you will learn how to build, train and tune your own convolutional neural networks from scratch. This can now be done in minutes using the power of TPUs. You will also explore multiple approaches from very simple transfer learning to modern convolutional architectures such as Squeezenet. This lab includes the necessary theoretical explanations about neural networks and is a good starting point for developers learning about deep learning. This lab uses Tensorflow 2.

• TensorFlow

Deploy a basic "Google Translate" app on Python 2 Cloud Run (Docker)

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Deploy a basic "Google Translate" app on Python 3 Cloud Run (Docker)

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Deploy a basic "Google Translate" app on Python 3 Cloud Functions

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Deploy a basic "Google Translate" app on Python 2 App Engine

In this codelab, you will learn how to use the Google Cloud Translation API with Python and either run locally or deploy to a Cloud serverless compute platform (App Engine, Cloud Functions, or Cloud Run).

Make a smart webcam in JavaScript with a TensorFlow.js pre-trained Machine Learning model

In this codelab, you’ll learn how to load and use one of the TensorFlow.js pre-trained models (COCO-SSD) and use it to recognize common objects it has been trained on.

• TensorFlow

TensorFlow.js Training in Node.js Codelab

In this codelab, you will learn how to build and train a baseball pitch estimation model using TensorFlow.js in a Node.js server, and serve metrics to a client.

• TensorFlow

Recognize text and facial features ML Kit: iOS

In this codelab, you’ll build an iOS app with ML Kit that uses on-device Machine Learning to recognize text and facial features in images.

• ML Kit

Build a Fraud Detection model on Cloud AI Platform with TensorFlow Enterprise and BigQuery

In this lab, you’ll directly ingest a BigQuery dataset and train a fraud detection model with TensorFlow Enterprise on Google Cloud AI Platform.

• TensorFlow

Build, train, and deploy an XGBoost model on Cloud AI Platform

In this lab you’ll walk through a complete ML workflow on GCP: ingesting data from BigQuery, building an XGBoost model in a Cloud AI Platform Notebooks instance, and deploying the model to AI Platform.

TensorFlow, Keras and deep learning, without a PhD

In this codelab, you will teach the computer to recognise handwritten digits with 99% accuracy, in 100 lines of Python / Keras code.

• TensorFlow

From Notebook to Kubeflow Pipelines with HP Tuning: A Data Science Journey

In this codelab, you will learn how to build and deploy complex data science pipelines with hyperparameter tuning on Kubeflow Pipelines, without using any CLI commands or SDKs.

From Notebook to Kubeflow Pipelines with MiniKF and Kale

In this codelab, you will learn how to build and deploy complex data science pipelines with Kubeflow Pipelines, without using any CLI commands or SDKs.

Build an AutoML Forecasting Model with Vertex AI

In this lab, you’ll learn about how to train and deploy a forecasting model using AutoML in Vertex AI.

Analyzing a financial ML model deployed on Cloud AI Platform with the What-if Tool

In this lab you’ll learn how to train an XGBoost model on a financial dataset, deploy it to Cloud AI Platform, and analyze it with the What-if Tool

• TensorFlow

PySpark for Natural Language Processing on Dataproc

This lab shows you how to use Spark MLlib and spark-nlp for performing machine learning and NLP on large quantities of data.

TensorFlow.js: Use Firebase Hosting to deploy and host a machine learning model at scale

In this codelab, you’ll learn how to use the Firebase infrastructure to deploy an ML model so it can be used and consumed on your website using TensorFlow.js

• TensorFlow

Training and hyperparameter tuning a PyTorch model on Cloud AI Platform

In this lab you’ll learn how to train your model in the Cloud with hyperparameter tuning. We’ll be showing you how to do this with PyTorch, but you can do this in any framework you’d like.

TensorFlow.js: Convert a Python SavedModel to TensorFlow.js format

In this codelab, you’ll learn how to take an existing Python ML model that is in the SavedModel format and convert it to the TensorFlow.js format so it can run in a web browser whilst also learning how to address common issues that may occur in conversions.

• TensorFlow

Building a financial ML model with the What-If Tool and Vertex AI

In this lab you’ll learn how to train an XGBoost model on a financial dataset, deploy it to Vertex AI, and analyze it with the What-if Tool

• TensorFlow

Kubeflow Pipelines - GitHub Issue Summarization

In this codelab, you will set up a Cloud AI Platforms Pipeline installation (Hosted KFP) with GKE, build and run ML workflows using Kubeflow Pipelines, and define and run pipelines from within an AI Platform Notebook (Jupyter).

• TensorFlow

Explaining a fraud detection model with Cloud AI Platform

In this lab you’ll build a tf.keras for identifying fraudulent transactions using TensorFlow, and then interpret the model’s results with Cloud’s Explainable AI SDK.

• TensorFlow

TensorFlow.js - Audio recognition using transfer learning

In this codelab, you will build a basic audio recognition network that can recognize your sounds and use it to control a slider in the browser. You will be using TensorFlow.js, a powerful and flexible machine learning library for Javascript.

• TensorFlow

Create a custom text classification model, and update your apps with it

In this codelab, you’ll learn how to enhance the model you built in the Get started with mobile text classification pathway, so you can have a model that works with your own data. You’ll then see how to update your Android and iOS apps with the new model

• TensorFlow
• Android

Update your app to use a spam filtering Machine Learning model

Learn to update an app with an ML model capable of basic comment spam filtering.

• TensorFlow
• Android

Build a Comment Spam Machine Learning model

In this codelab, you’ll learn how a machine learning model capable of filtering spam from other comments is created.

• Android
• TensorFlow

Build a basic messaging style app

In this codelab, you’ll learn to build a simple messaging app with a text box and a send button.

• TensorFlow
• Android

Create a custom model for your image classifier

In this codelab, you’ll train an ML model to recognize specific flowers.

• Android
• TensorFlow

Integrate a Custom Model into your App

In this codelab, you’ll learn how to integrate a custom image classification model into an Android or iOS app using ML Kit Custom Models.

• TensorFlow
• Android

Recognize Flowers with TensorFlow Lite on Android

In this codelab you will take an image classifier, and run it on an Android phone using TensorFlow Lite.

• Android
• TensorFlow

AI Speech Recognition with TensorFlow Lite for Microcontrollers and SparkFun Edge

In this codelab, you’ll learn to run a speech recognition model using TensorFlow Lite for Microcontrollers on the SparkFun Edge, a battery powered development board containing a microcontroller.

• TensorFlow

Android 11 - Week 2 - Machine Learning

Machine learning provides your apps with the ability to progressively learn and improve from experience. This pathway introduces you to the wide variety of machine learning tools and methods Android 11 provides.

• TensorFlow
• Android
• ML Kit

Program neural networks with TensorFlow

Learn everything that you need to know to demystify machine learning, from the first principles in the new programming paradigm to creating convolutional neural networks for advanced image recognition and classification that solve common computer-vision problems.

• TensorFlow

Build a custom, responsive chatbot in Google Cloud

Learn to build a custom frontend for a Dialogflow chatbot to create a natural conversational experience for users, then enhance it with Cloud Vision so that it can analyze uploaded images and include that analysis in responses.

• Dialogflow