Fully managed AI platform / environment. The researcher focuses on their code and skips the burden of managing the infrastructure. Integrated Jupyter notebooks, model training, and MLOps tools.

Features:Pre-built deep learning containers, distributed training, model deployment, AutoML

Use Cases:Model prototyping, training pipelines, model serving, experiment tracking

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GPUs

Available GPUs:

GPU Models:NVIDIA H100, NVIDIA L4

Specs:H100: 80GB HBM3e | L4: 24GB GDDR6

Use Cases:LLM training, generative AI, deep learning inference, video processing

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Cloud Storage & Databases

Scalable Storage

Scalable object storage for training data, models, and checkpoints. Fully managed database solutions for structured and unstructured data.

Cloud Storage:Object storage with multiple classes (Standard, Nearline, Coldline, Archive)

Databases:Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore

Features:Automatic lifecycle management, versioning, encryption at rest

Cloud Storage Databases

BigQuery

Serverless Data Warehouse

A fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. It allows for super-fast SQL queries and real-time analytics.

Features:Petabyte-scale, real-time analytics, automatic high availability, built-in machine learning

Pricing:Pay-per-query or flat-rate pricing models

Use Cases:Data warehousing, business intelligence, log analysis, predictive analytics

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BigQuery ML

ML Inside Your Data Warehouse

Extends BigQuery's capabilities by allowing users to create and execute machine learning models directly within BigQuery using standard SQL queries. It simplifies ML development by bringing ML to where the data resides.

Supported Models:Linear regression, logistic regression, XGBoost, TensorFlow models, AutoML

Features:No data movement, SQL-based ML, automatic feature preprocessing, model evaluation

Use Cases:Sales forecasting, customer churn prediction, anomaly detection, recommendation systems