On-demand activities
Find the right on-demand learning activities for you. Labs are short learning activities that teach you a specific lesson by giving you direct, temporary, hands-on access to real cloud resources. Courses are longer activities, consisting of several modules made of videos, documents, hands-on labs and quizzes. Finally, quests are similar, but are usually shorter and contain only labs.
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Lab Featured HTTP Google Cloud Functions in Go
In this lab you'll build an HTTP Cloud Function in Go.
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Lab Featured Stream Processing with Cloud Pub/Sub and Dataflow: Qwik Start
This quickstart shows you how to use Dataflow to read messages published to a Pub/Sub topic, window (or group) the messages by timestamp, and Write the messages to Cloud Storage.
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Lab Featured Prepare Data for ML APIs on Google Cloud: Challenge Lab
This challenge lab tests your skills and knowledge from the labs in the Prepare Data for ML APIs on Google Cloud course. You should be familiar with the content of the labs before attempting this lab.
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Lab Featured Use Vertex AI Studio for Healthcare
In this lab, you will learn how to use Vertex AI Studio to create prompts and conversations with Gemini's multimodal capabilities in a healthcare context.
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Lab Featured Analyze Customer Reviews with Gemini Using SQL
Learn how to use BigQuery Machine Learning with remote models (Gemini) to analyze customer reviews using SQL.
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Lab Featured Mitigate Bias with MinDiff in TensorFlow
This lab helps you learn how to mitigate bias using MinDiff technique by leveraging TensorFlow Model Remediation library.
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Lab Featured Fraud Detection on Financial Transactions with Machine Learning on Google Cloud
Explore financial transactions data for fraud analysis, apply feature engineering and machine learning techniques to detect fraudulent activities using BigQuery ML.
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Lab Featured Build an LLM and RAG-based Chat Application with AlloyDB and Vertex AI
In this lab, you create a chat application that uses Retrieval Augmented Generation, or RAG, to augment prompts with data retrieved from AlloyDB.
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Lab Featured Create Text Embeddings for a Vector Store using LangChain
In this lab, you learn how to use LangChain to store documents as embeddings in a vector store. You will use the LangChain framework to split a set of documents into chunks, vectorize (embed) each chunk and then store the embeddings in a vector database.