1. Vertex AI
Google Cloud’s new unified ML platform
Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.
«Google Cloud unveils Vertex AI, one platform, every ML tool you need»
cloud.google.com/blog/products/ai-machine-learning/google-cloud-launches-vertex-ai-unified-platform-for-mlops
2. Vertex AI Workbench
The single development environment for the entire data science workflow.
cloud.google.com/vertex-ai-workbench
3. Deep Learning VM Image
Preconfigured VMs for deep learning applications
cloud.google.com/deep-learning-vm
Deep Learning VM Images are virtual machine images optimized for data science and machine learning tasks.
All images come with key ML frameworks and tools pre-installed, and can be used out of the box on instances with GPUs to accelerate your data processing tasks.
cloud.google.com/deep-learning-vm/docs
Deep Learning VM Images is a set of virtual machine images optimized for data science and machine learning tasks.
All images come with key ML frameworks and tools pre-installed.
You can use them out of the box on instances with GPUs to accelerate your data processing tasks.Deep Learning VM images are available to support many combinations of framework and processor.
There are currently images supporting TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, with versions for both CPU-only and GPU-enabled workflows.
cloud.google.com/deep-learning-vm/docs/introduction
4. Deep Learning Containers
Preconfigured and optimized containers for deep learning environments.
cloud.google.com/deep-learning-containers
Deep Learning Containers are a set of Docker containers with key data science frameworks, libraries, and tools pre-installed.
These containers provide you with performance-optimized, consistent environments that can help you prototype and implement workflows quickly.
cloud.google.com/deep-learning-containers/docs
5. Vision AI
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.
6. AutoML
Train high-quality custom machine learning models with minimal effort and machine learning expertise.
AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs.
Build your own custom machine learning model in minutes.
7. AI Infrastructure
cloud.google.com/ai-infrastructure
Options for every business to train deep learning and machine learning models cost-effectively.
- AI accelerators for every use case, from low-cost inference to high-performance training
- Iterate faster with high-performance Cloud GPUs and Cloud TPUs
- Simple to get started with a range of services for development and deployment
7.1. Cloud TPU
Train and run machine learning models faster than ever before.
7.2. Cloud GPUs
High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization.
NVIDIA K80, P100, P4, T4, V100, and A100 GPUs provide a range of compute options to cover your workload for each cost and performance need.
8. AutoML Tables
AutoML Tables enables your entire team to automatically build and deploy state-of-the-art machine learning models on structured data at massively increased speed and scale.
cloud.google.com/automl-tables
AutoML Tables helps you create clean, effective training data by providing information about missing data, correlation, cardinality, and distribution for each of your features.
And because there's no charge for importing your data and viewing information about it, you don't incur charges from AutoML Tables until you start training your model.
cloud.google.com/automl-tables/docs/features
9. Cloud Inference API
Quickly run large-scale correlations over typed time-series datasets.
cloud.google.com/inference
- Index and load a dataset consisting of multiple data sources stored on Google Cloud Storage.
- Execute Inference queries over loaded datasets, computing relations across matched groups (see below for data organization).
- Unload or cancel the loading of a dataset.
- Get simple status updates for a dataset sent for processing.