This repo gives you hints and demos to jumpstart on vertex ai.

cloud_orbit_demo_1_apis.ipynb
explain the basic vertex ai apis

• vision.image
• sentiment_analysis
• entities recognition (nlp)
• google translate

cloud_orbit_demo_2_automl_endpoint_fetch.ipynb
explain the usage of automl.

• fetching answer from a deployed endpoint

cloud_orbit_demo_3_vertex_pipeline.ipynb
explain vertex ai pipelines

• creating components
• creating a pipeline with the components
• compiling the pipeline
• run a pipeline job
• use a yaml file to do programatic pipelines
• creating a pipeline for machine learning
• creating a tabular dataset based on big query
• creating an auto-ml model
• callling the previous component to know if the model's performance is good enough
• using a conditional test from "dsl" library to decide or not to deploy
• creating an endpoint to serve the model
• visualizing the pipeline in vertex's user interface :

cloud_orbit_demo_4_gpu_vs_cpu.ipynb
explore gpu calculus and endpoint deploiement

• testing cpu vs gpu calculus on vertex ai
• seeing that gpu is slower on small dataset, but faster in big ones
• creating a pipeline for data preparation
• create a pipeline model
• create a pipeline api endpoint with the created model
• testing the created endpoint

video_transcription_demo_github.ipynb
explain how to do an automatic summary of a video, with timestamped parts. Example of result here

• import and upload a video in a bucket
• use vidéo transcription for speech to text
• use genai and prompt-engineering with text-bison@002 to do a chapitrage
• output as html