Date d'abonnement : 2024
Model Armor: Securing AI Deployments
Earned mars 15, 2026 EDT
Machine Learning Operations (MLOps): Getting Started
Earned juil. 9, 2024 EDT
[Depricated] Build, Train and Deploy ML Models with Keras on Google Cloud
Earned juil. 4, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned juil. 1, 2024 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 apps. Explore other content in the Gemini Agent Ready (GEAR) program.
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.