Gemini API for Developers: Build and Deploy AI Apps
Gemini for developers — not the chat window, the API: authentication, the SDK, cost control, and getting an agent running on Google Cloud. Most Gemini content shows you the web interface. This one assumes you want to build something.
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⏱️ Chapters:
0:00:00 Introduction
0:02:19 Introduction to Google Cloud Platform
0:08:28 The Google Model Family
0:16:25 Accessing Google Models: Your Options
0:21:06 Google AI Studio: Introduction
0:30:39 API Keys Explained
0:34:46 API Keys and Projects
0:40:12 Create Your API Key
0:42:52 Access Gemini from a Python Program
0:48:23 Build an AI App with Google AI Studio
0:52:06 Deploy Your App
0:56:37 Introduction to Vertex AI
1:01:09 Vertex AI Studio
1:06:42 Vertex AI: Create an API Key
1:14:50 Vertex AI: Access a Model with an API Key
1:19:10 Accessing Gemini with a Service Account Key (Part 1)
1:24:22 Accessing Gemini with a Service Account Key (Part 2)
1:30:33 Accessing Gemini as an Authenticated User
1:37:34 Using Colab Enterprise
1:45:13 Workbench: Create an Instance
1:48:45 Using Workbench
1:55:14 Introduction to the google-genai SDK
2:00:50 Count Your Tokens
2:10:37 Limit the Thinking Budget
2:15:43 Limit Output Tokens
2:21:21 Temperature and Randomness Settings
2:24:30 Safety Settings
2:26:45 Adding System Instructions
2:28:55 Using Streaming Responses
2:31:50 Create an Image with Nano Banana
2:35:54 Edit an Image with Nano Banana
2:37:25 Generate a Video with Veo
2:40:52 Summarize a YouTube Video using Gemini
2:43:47 Tools with LLMs: Google Search as a Grounding Tool
2:48:51 Tools with LLMs: Custom Function Calling
2:53:42 Demo of the Agent
3:01:07 Introduction to Chainlit
3:07:15 Build a Simple Chatbot with Chainlit and Gemini
3:14:28 Email Tool Setup
3:17:55 Create a Resume Reviewer Agent
3:24:03 Dockerize the Application
3:30:31 Deploy the Agent with Artifact Registry and Cloud Run
3:39:05 Gemini CLI: Introduction and Installation
3:42:08 Gemini CLI: Authentication with Google ID
3:46:03 Exploring Gemini CLI Options
3:54:03 Gemini CLI: Authenticating with an API Key
3:57:39 Gemini CLI: Authenticating with Vertex AI
4:01:11 Using Gemini CLI in Cloud Shell
4:08:08 Install Gemini Code Assist
4:10:42 Explore Gemini Code Assist
Basic Python and some familiarity with Google Cloud is enough — everything else is built up from there.
💰 On costs, and this matters more here than in most courses: Google AI Studio has a free tier that covers learning, but Vertex AI, Colab Enterprise, Workbench instances, Artifact Registry and Cloud Run all bill by usage. A Workbench instance left running costs money whether you're using it or not. Set a budget alert on your GCP project before you start, and shut down instances when you finish a session. The token-control sections (2:00:50 onward) exist precisely because this gets expensive quietly.
🔐 On credentials: never commit a service account key or API key to a repository, and don't put either in client-side code. The authentication sections cover why each method exists — use the most restricted one that does the job, not the easiest one to set up.
ℹ️ This course is independent and is not affiliated with, endorsed by, or sponsored by Google. Gemini, Vertex AI and Google Cloud are trademarks of Google LLC. For educational purposes only. Model names, SDK methods and console layouts change frequently — check Google's current documentation if something doesn't match.
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