Cut the learning curve and integration effort
Run smart and fast at reduced costs
AI Training, Inference and Analytics in the Cloud
TensorFlow and PyTorch integration
Inference & Analytics
Scarlet AI Training,
Inference & Analytics
No data sharing. Hourly and usage-based pricing
Craft your AI and data strategy
Train and visualize TensorFlow and PyTorch AI models with ease and flexibility. Simply upload your training scripts to the platform and create training jobs that are automatically managed. With one click, you can export your trained models for inference.
It supports scheduling, multiple run history, instance type choice, per run visualization and cost/saving estimates.
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.
Unlock the value of your unstructured data
We combine powerful integration and intelligent resource management to give you high performance at low running cost
Organize your unstructured data
Classify and label your unstructured data from your cloud buckets, such as pictures using a vision AI model, and organize them by category in a target location of your choice
Detect sensitive content
Extract text content from different types of documents, label them using an NLP model and receive notifications if certain type of content is detected
Triage large amounts of data fast
Quickly ingest, analyze using AI and triage large amounts of regularly generated data, such as log files. Retain only the relevant objects in a target location of your choice
Clean up duplicate data automatically
Detect duplicate objects across multiple buckets and clean up unneeded copies using a rule of your choice, for example keeping only the most recent copy or the copy in a specific bucket.