AI & Machine Learning Platform
Build, train, and deploy machine learning models at scale.
Supported Frameworks
Platform Features
Everything you need to build AI and ML applications.
High-Performance Computing
Access GPU and TPU clusters for training large models
MLOps Pipeline
Automated ML workflow management and deployment
Pre-trained Models
Ready-to-use models for common AI tasks
Data Processing
Scalable data processing and feature engineering
Popular Use Cases
See what you can build with our AI & ML platform.
Computer Vision
Image and video analysis with deep learning.
- Object Detection
- Image Classification
- Face Recognition
- Video Analysis
Natural Language
Text processing and language understanding.
- Text Classification
- Named Entity Recognition
- Sentiment Analysis
- Language Translation
Predictive Analytics
Data-driven predictions and forecasting.
- Demand Forecasting
- Anomaly Detection
- Risk Analysis
- Price Optimization
Solution Bundles
Pre-configured bundles for different ML workloads.
AI Starter Bundle
Essential tools for AI and machine learning workloads
- GPU-accelerated Computing
- ML Model Management
- Data Pipeline Integration
Development Environments
Choose your preferred ML development environment.
Jupyter Lab
Interactive development environment for notebooks, code, and data.
- Code Intelligence
- Git Integration
- Real-time Collaboration
VS Code Server
Full VS Code experience in the browser with ML extensions.
- Debugger Support
- Extension Marketplace
- Remote Development
MLflow
Platform for the complete machine learning lifecycle.
- Experiment Tracking
- Model Registry
- Model Serving
Available Hardware
High-performance hardware for training and inference.
GPU Instances
NVIDIA A100
80GB GPU Memory, 7 TFLOPS FP64
NVIDIA V100
32GB GPU Memory, 7.8 TFLOPS FP64
NVIDIA T4
16GB GPU Memory, Optimized for Inference
TPU Instances
TPU v4
275 TFLOPS, Perfect for Large Models
TPU v3
90 TFLOPS, Cost-effective Training
ML Pipeline Stages
End-to-end machine learning workflow.
Data Preparation
- Data Validation
- Feature Engineering
- Data Versioning
Model Training
- Distributed Training
- Hyperparameter Tuning
- Experiment Tracking
Model Evaluation
- Performance Metrics
- A/B Testing
- Model Comparison
Model Deployment
- Model Serving
- Version Control
- Monitoring