.png)
https://gamma.app/docs/5-Powerful-Google-Tools-Every-Gen-AI-Developer-Should-Explore-i6kzv9kepcr4a6q
ful Google Tools Every Gen-AI Developer Should Explore
🔧 Powerful Google Tools Every Gen-AI Developer Should Explore
Whether you're building the next ChatGPT, designing intelligent apps, or integrating AI into business workflows, Google offers a suite of powerful tools that can speed up development, boost productivity, and enable innovation. Here’s a curated list of essential Google tools every Gen-AI developer should explore:
1. Vertex AI
Train, deploy, and manage ML models at scale.
-
Fully managed ML platform on Google Cloud.
-
Supports custom and pre-trained models, including large language models (PaLM 2, Gemini).
-
Built-in MLOps tools (e.g., pipelines, feature store).
-
Integrates with BigQuery, AutoML, and more.
2. Colab (Google Colaboratory)
Jupyter notebooks in the cloud – no setup required.
-
Ideal for prototyping and experimenting with models.
-
Free GPU and TPU support for training/testing.
-
Collaborate in real-time with teammates.
-
Supports integration with Google Drive and GitHub.
3. TensorFlow & TensorFlow Hub
The core ML library by Google.
-
Extensive ecosystem for deep learning, NLP, and Gen-AI tasks.
-
TensorFlow Hub offers reusable pre-trained models.
-
Strong community and documentation.
🔗 Visit TensorFlow
🔗 TensorFlow Hub
4. T5 & FLAN Models via Hugging Face or Vertex AI
Google's top-tier transformer models.
-
T5 (Text-To-Text Transfer Transformer) and FLAN-T5 fine-tuned on instructions.
-
Can be deployed via Hugging Face or Vertex AI for various NLP tasks.
5. PaLM API / Gemini API (via Google AI Studio)
Access powerful LLMs through simple APIs.
-
Easy-to-use endpoints for text, chat, embeddings, and more.
-
Google AI Studio offers a playground to test prompts and tune outputs.
-
PaLM 2 and Gemini 1.5 models now support longer context and multimodal inputs.
🔗 Try Gemini with Google AI Studio
6. BigQuery ML
Run machine learning directly inside your data warehouse.
-
Train and deploy models using SQL.
-
Great for Gen-AI use cases that rely on tabular data + prompt templates.
-
Integrates with Looker, Vertex AI, and Data Studio.
🔗 Get started with BigQuery ML
7. Google Cloud Functions + Gen-AI
Deploy lightweight AI-powered microservices.
-
Serverless compute for AI-based APIs, trigger-based workflows.
-
Useful for chaining model responses or building RAG pipelines.
8. Dialogflow CX
Build intelligent conversational agents.
-
Supports voice and chat integrations.
-
Can be powered by generative models like PaLM/Gemini for smarter conversations.
-
Multilingual, omnichannel support.
9. Google Sheets + Apps Script
Use LLMs inside spreadsheets.
-
Automate tasks with Gen-AI inside Google Sheets using custom functions.
-
Connect with PaLM or Gemini via APIs using Apps Script.
-
Great for internal tools or no-code/low-code AI use cases.
10. Google Cloud’s Generative AI Solutions Gallery
Prebuilt templates & code for Gen-AI tasks.
-
Examples include summarization, classification, RAG, chatbots, and more.
-
Jumpstart your projects with plug-and-play pipelines.
🧠 Bonus: Gemini Code Assist (Preview)
Google’s upcoming AI-powered coding assistant is expected to tightly integrate with VS Code, JetBrains, and Google’s own development tools. It’s currently in preview and could be a serious competitor to tools like GitHub Copilot.
🚀 Final Thoughts
Google’s Gen-AI ecosystem is expanding fast. Whether you're building applications, analyzing data, or prototyping new models, these tools can make your work faster, more scalable, and more innovative.
Pro tip: Combine tools like Vertex AI + BigQuery + Colab for full-stack AI workflows.
