STEPHANIE WONG: All right, we've heard developers and data
scientists.
Many of you have been searching for one environment
to build custom machine learning models, a unified experience
with custom code and prepackaged models for training,
along with ML operations capabilities built-in.
A single experience with an API, SDK, GUI, and notebooks.
Google Cloud has been leading with its AI platform and AutoML
products, like AutoML Vision, Tables, and Natural
Language, because they offer an end-to-end ML lifecycle.
But we understand that you don't want
to have separate experiences for the AutoML training
path and the Custom AI platform path.
So we're announcing Vertex AI, Google Cloud's managed ML
platform, which unifies our existing offerings
into a single experience for experimentation,
versioning, and deploying ML AI models
into production environments.
You'll be able to seamlessly manage and deploy your models
because we are bringing together a new workflow--
UI, API, and SDKs for API platform training, AI platform
prediction, AutoML Tables, AutoML Vision, AutoML Video
Intelligence, AutoML Natural Language, Explainable AI,
and Data Labeling.
Each of these services are now features
of AI Platform Unified.
Vertex AI is the evolution of AI Platform Unified.
Additionally, Vertex AI includes new MLOps features, including
Vertex Experiments to track, analyze, and discover
ML experiments for faster model selection;
Vertex Vizier, which provides optimized values
for hyperparameters to maximize a model's predictive accuracy;
Vertex Pipelines, which streamlines
building and running ML pipelines to simplify MLOps;
and a lot more than that.
You can use Vertex AI to manage the following stages in the ML
workflow.
First, you can define and upload a data set.
Then train an ML model on your data,
either using AutoML or a custom training
on different machine types and GPUs.
And get model evaluation and tuning hyperparameters
for custom models.
And data labeling jobs will let you
request human labeling for a data set for custom ML models.
You can also upload and store your model in Vertex AI
and deploy your trained model and get an endpoint
for serving predictions.
And then finally, send prediction requests
to your endpoint, specify your prediction traffic split
in your endpoint, and manage your models and endpoints
from there.
To interact with Vertex AI, you can
use notebooks that are actually prepackaged with JupyterLab
and also with deep learning packages.
The Google Cloud console is the UI
to work with your ML resources and get logging and monitoring.
You can also use the Cloud client libraries
for an optimized developer experience
for a set of languages or the Google API client libraries
to access the full Vertex AI API by using other languages,
like Dart.
Or you can use the REST API for managing jobs, models,
endpoints, and predictions.
This is really exciting because you have one experience
and interface to use both AutoML or create custom models.
You have a shared infrastructure, data modeling
UX and API layer, and have the ability
to quickly move between data science and production.
You get both the notebook-based development version
and a hosted cloud production version.
For ML novices, AutoML gives you the explainability
and automatic provisioning.
But then as you become more experienced,
you'll be able to manage constraints and make
your own fine-grained decisions using the rest of Vertex
AI, all from one place.
So check out cloud.google.com/vertex-ai/docs
for Vertex AI tutorials on training image data,
doing custom training, and bringing in structured data.
And you can also get code samples
for Python, Java, Node.js, and more.
And join us for the Digital Applied ML Summit
at g.co/appliedmlsummit on June 10 for Vertex AI technical
tutorials and interactive sessions with leading
innovators and Kaggle grandmasters.
This is Stephanie Wong.
See you all next time.