Join Sign in

Apply your skills in Google Cloud console

Mansoor AK

Member since 2026

Bronze League

2768 points
Model Armor: Securing AI Deployments Earned апр. 29, 2026 EDT
Introduction to Security in the World of AI Earned апр. 29, 2026 EDT
Responsible AI for Developers: Privacy & Safety Earned апр. 29, 2026 EDT
Responsible AI for Developers: Fairness & Bias Earned апр. 29, 2026 EDT
Responsible AI for Developers: Interpretability & Transparency Earned апр. 29, 2026 EDT
Arcade Voyage: Modern Application Development Earned апр. 29, 2026 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned апр. 28, 2026 EDT
Machine Learning Operations (MLOps) for Generative AI Earned апр. 28, 2026 EDT
Dialogue Design Earned апр. 28, 2026 EDT
Arcade Trail: Data Migration Earned апр. 28, 2026 EDT
Arcade Adventure: GKE Operations and Networking Earned апр. 27, 2026 EDT

This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI applications.

Learn more

Artificial Intelligence (AI) offers transformative possibilities, but it also introduces new security challenges. This course equips security and data protection leaders with strategies to securely manage AI within their organizations. Learn a framework for proactively identifying and mitigating AI-specific risks, protecting sensitive data, ensuring compliance, and building a resilient AI infrastructure. Pick use cases from four different industries to explore how these strategies apply in real-world scenarios.

Learn more

This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

Learn more

This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud products and open source tools.

Learn more

This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

Learn more

On Google Cloud, building an app usually means handling more than just the code. You’ll need to deploy it, connect services, and keep things running properly. In this voyage, you’ll host a web app, set up a deployment pipeline, and build a REST API with Cloud Run. You’ll also try out Flutter and set up a Python development environment. You’ll work with VPC networks, load balancing, and clean up unused resources along the way. It’s a practical set of tasks that reflects how apps are actually run on Google Cloud.

Learn more

This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

Learn more

This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.

Learn more

78%—or nearly 8 in 10—business leaders say Google Cloud helps them stay ahead in the age of AI. A big part of that comes down to how teams build and connect intelligent systems. Here, you’ll build and manage conversational agents, and use speech-to-text and translation APIs to handle different types of input. You’ll also move from monolithic apps to microservices on GKE, connect workflows using webhooks, and use observability tools to keep track of what’s happening. IAM comes in as well to manage access where needed. It’s a practical look at how these pieces are used together in real setups.

Learn more

A lot of cloud work comes down to moving data, managing access, and making sense of what’s happening behind the scenes. In this trail, you’ll migrate a MySQL database to Google Cloud, work with IAM permissions using gcloud, and analyze network traffic with VPC Flow Logs. You’ll also store and manage media files, prepare data with Dataprep, and build reports using Looker Studio and LookML. There’s even a lab on measuring Speech-to-Text accuracy. It’s a mix of tasks that shows how data is handled, monitored, and used across different parts of Google Cloud.

Learn more

Google Kubernetes Engine (GKE) is all about running and managing containerized applications without worrying too much about the underlying setup. In this adventure, you’ll get hands-on with how things actually work—managing workloads, debugging issues, and trying out autoscaling. You’ll also explore Autopilot, run load tests, and work with private clusters and network access. By the end, you’ll have a clearer sense of how GKE setups are put together and handled in practice.

Learn more