Scarlet Features
Model training, LLM fine-tuning
Scarlet offers training or fine-tuning of TensorFlow and PyTorch models with ease and flexibility. It supports scheduling, multiple run history, instance type choice, visualization and cost/saving estimate.
AI inference and analytics
Scarlet Platform enables you to run models trained or fine-tuned on the platform, as well as imported TensorFlow and PyTorch models. You can classify, tag, manage and instantly search your training data to optimize your workflows.
Model development
Scarlet Platform integrates JupyterLab to accelerate your model development. You can run your Jupyter notebooks or Python scripts interactively or execute them automatically.
Flexible settings and scheduling
Easily select from a wide selection of high-performance GPUs or CPUs configurations, import your models from the cloud, Git repositories or your local computer. Scarlet platform lets you run your training on the spot or schedule your runs in advance.
Scalable and cost effective
With its dynamic GPU management, Scarlet Platform enables you to save up to 70% of your training costs and ensures that your GPUs are only run when you need them.
Visualization and insights
Full training history with model snapshots and cost reports lets you easily view, compare and optimize your runs. Scarlet Platform also has TensorBoard integration to visualize and analyze your training results.
Get Scarlet Platform
Get the latest version as a SAAS with our free trial
Deploy in your own Virtual Private Cloud on AWS
Testimonials
DataMacaw provided our team with a great environment to train our AI models. Their platform is intuitive, easy to use and customize to our needs. On top of that you get access to good GPUs for a lot cheaper than on-demand instances on AWS. I totally recommend it!
DataMacaw’s Scarlet platform has been a joy to work with, and has transformed how we train models at Patronus AI. The best part is probably being able to train models on cost-efficient AWS spot instances, while retaining saved model weights and results in a centralized GUI. It’s easy to manage training jobs, and is more cost-effective than AWS’s SageMaker machine learning platform.
We also love its native TensorBoard integration, handy job completion notifications, and helpful support. I would recommend DataMacaw to any team with a complex machine learning workflow.
It is a very impressive product, Scarlet did analyze my S3 buckets and present very coherently. The UI is very polished and easy to get around.
Use Cases
We helped teams develop, fine-tune their LLMs, and train their machine learning models in multiple industries including:
- Industrial
- Climate
- Life Sciences
- Legal