Streamlined MLOps

Concentrate on developing ML models without the hassle of handling infrastructure.

Experiment Logging

Record and version your ML experiments to ensure they can be reproduced.

Hyperparameter Tuning

Fine-tune your hyperparameters to achieve optimal model performance.

Data Management

Track and version your data to support collaboration and traceability.

Automated CI/CD for ML

Streamline your ML workflows with automated CI/CD pipelines.

Event-Driven Workflows

Initiate pipelines based on specific events or schedules to automate your ML workflows.

Model Management

Track and version your models to facilitate seamless transitions between team members.

Collaborative Workflows

Enable collaboration between data scientists and domain experts through human-in-the-loop workflows.

Data Labeling and Searching

Tag and search your data files for easy access and analysis.

Version Control with Git

Ensure reproducibility by managing your ML code and configurations with Git.

Flexible Framework and Language Support

Utilize any ML framework or programming language you prefer (e.g., Python, R, TensorFlow, PyTorch, sci-kit-learn, etc.).

Multi-Technology Orchestration

Deploy ML workloads on Virtual Machines, Kubernetes, Slurm, or any other technology within your environment.

Cloud-Agnostic Setup

Run your ML workloads on any cloud provider or on-premises infrastructure.

Real-Time Cost and Usage Monitoring

Track your usage and expenses in real time to optimize spending.

Advanced-Data Security

Secure your data with advanced security measures such as rest and transit encryption.

Comprehensive Access Control

Manage access levels across projects, data, and computational resources, integrated with your SSO framework.

Scalable Resources

Automatically scale your computing resources up or down based on your requirements to control costs.

Parallelized Training

Accelerate training times by running models on multiple nodes simultaneously using both model and data parallelism.