Affordable Pricing Just for You
Fast-Track Your Journey from Solutions to Production.
StageFlow
Manage Datasets, Experiments, & Models
Streamline Runtimes Management
One-click Model Import
Access Prebuilt Data Connectors
Utilize Prebuilt Template Solutions
Seamlessly Integrate with GitHub
Build, Release, & Deploy with Ease
Enjoy Limited Project Spaces
Unlimited Parallelization
Unlock Advanced Capabilities for Growing Teams.
Create Solutions On The Platform
Notebook IDE
Access Over 200 Connectors
Use Prebuilt Templates for HPO & Fine-tuning
Customize Data, Models, & Feature Stores
Leverage Advanced Scheduling Capabilities
Utilize Prebuilt Infrastructure Controls
Enjoy Limited Autoscaling Support
Monitor with Advanced Telemetry & Metrics
Dedicated Customer Success Manager
Unmatched Scalability & Enterprise-Grade Access Control for Large Organizations.
Advanced Customization Options
Build Your Templates
Organizational Controls
Define Custom Roles
Ensure Advanced Security & Controls
Unlimited Organizations & Solutions
Cost Controls & Approval Queue Management
Audit Logs & Tracing
GPU Sharing with Time-Sliced & Multi-Instance Options
Custom Solution Profiles
Dedicated Consulting Hours
CAI Platforms simplify the complexity of managing ML infrastructure, allowing you to focus on creating and deploying models.
Concentrate on developing ML models without the hassle of handling infrastructure.
Record and version your ML experiments to ensure they can be reproduced.
Fine-tune your hyperparameters to achieve optimal model performance.
Track and version your data to support collaboration and traceability.
Streamline your ML workflows with automated CI/CD pipelines.
Initiate pipelines based on specific events or schedules to automate your ML workflows.
Track and version your models to facilitate seamless transitions between team members.
Enable collaboration between data scientists and domain experts through human-in-the-loop workflows.
Tag and search your data files for easy access and analysis.
Ensure reproducibility by managing your ML code and configurations with Git.
Utilize any ML framework or programming language you prefer (e.g., Python, R, TensorFlow, PyTorch, sci-kit-learn, etc.).
Deploy ML workloads on Virtual Machines, Kubernetes, Slurm, or any other technology within your environment.
Run your ML workloads on any cloud provider or on-premises infrastructure.
Track your usage and expenses in real time to optimize spending.
Secure your data with advanced security measures such as rest and transit encryption.
Manage access levels across projects, data, and computational resources, integrated with your SSO framework.
Automatically scale your computing resources up or down based on your requirements to control costs.
Accelerate training times by running models on multiple nodes simultaneously using both model and data parallelism.
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