2023 Google I/O [10 ways of using machine learning with Google Cloud]

This page summarizes the following Google I/O video

https://www.youtube.com/watch?v=oQMgqMRR-io

1. Teachable Machine

• Browser based tool for image or audio classification
• Data from webcam, microphone, or via upload
• Great for prototyping
• Models are exportable
• Exported model is TensortFlow-like so it can be deployed anywhere such as on mobile, web, or even Cloud

2. AutoML in Vertex AI

• Machine-learning tool for developers on Google cloud
• Used to train custom enterprise grade models
• No ML expertise required
• High quality models
• Build models to predict on images, video, text, or even tabular data
• Easy to deploy the models for large scale batch and real time predictions

3. ML APIs

• ML APIs for developers
• Embed machine learning capabilities directly into application with just a single API call
• Use powerful pre-trained models by Google
• Vision API, Natural Language API, Video Intelligence API
• Includes latest generative AI APIs like text to image and code completion
• Great for common tasks like sentiment analysis and text and person detection in video

4. Generative AI Studio

• Use prompts to generate images and text
• Chat, sumarize text, and more
• User-Interface or API
• Prompt-tune and fine tune models

5. Vertex AI Model Garden

• Single environment to interact with a variet of model types
• Models will span across modalities
• Deploy to endpoints with ease

6. Four New Foundation Models in Model Garden

1. Code Generation & Completion: Enhance the software development leveraging your own code base
2. Image Generation: Generate high-quality images with low latency, edit and iterate to your specifications
3. Universal Speech: Unlocks the power of voice and highly accurate speech transcriptions
4. Embeddings: Extract high quality semantic information from unstructured data

7. New Tuning Capability: Reinforcement Learning from Human Feedback (RLHF)

• Use human feedback to increase your model's usefulness
• Makes easy to optimize machine learning model performance with human feedback
• Improve pre-trained LLM without increasing the model size

8. BigQuery ML

• ML directly in your data warehouse
• Use popular ML models in SQL
• Use Vertex AI endpoints or ML APIs on your BigQuery data
• MLOps integrations

9. Vertex AI custom training

• JupyterLab with fully customizable compute
• Enterprise-grade
• Use Colab for quick use of Python

10. Vertex AI Matching Engine

• Fast, scalable similarity search
• Vector database for embeddings
• Great for variety of data modalities
• Enterprise-grade