What is DataMacaw Scarlet Platform?
DataMacaw Scarlet Platform provides an environment for training and fine-tuning generative AI and machine learning models. It leverages high-performance cloud GPUs, specifically the latest generation from Nvidia, allowing users to improve model performance while aiming to reduce running costs by up to 70%. The platform is designed for accessibility, eliminating the need for specialized cloud infrastructure knowledge and offering quick setup via a SaaS option or deployment within a user's own Virtual Private Cloud (VPC).
This end-to-end solution supports the entire model lifecycle, including development, training, fine-tuning, and inference. It integrates tools like JupyterLab for running notebooks and Python scripts. Users can manage training jobs, utilize distributed inference, access advanced data analytics features, and monitor performance through features like training history, model snapshots, cost reports, and TensorBoard integration.
Features
- Model training, LLM fine-tuning: Fine-tune generative AI models and train Machine Learning models on high-performance Nvidia GPUs.
- AI inference and analytics: Run trained or fine-tuned models on user data sources; manage and search training data.
- Model development: Integrates JupyterLab for running Jupyter notebooks and Python scripts.
- Flexible settings and scheduling: Select from various high-performance instances; run jobs interactively or schedule them.
- Scalable and cost effective: Aims to save up to 70% on training costs and ensures GPUs only run when necessary.
- Visualization and insights: Maintains training history, model snapshots, cost reports, and integrates with TensorBoard.
Use Cases
- Fine-tuning Large Language Models (LLMs) with custom datasets.
- Training complex machine learning models on cloud GPUs.
- Reducing the operational costs associated with AI model training.
- Deploying and managing AI models within a secure Virtual Private Cloud (VPC).
- Running AI inference tasks using models trained or fine-tuned on the platform.
- Developing AI models efficiently using integrated tools like JupyterLab.
- Managing and analyzing AI training data and results.
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