MLSteam AI Platform from Myelintek is a robust deep neural networks (DNN) lifecycle solution, encompassing development and deployment stages, with an integrated approach to Machine Learning Operations (MLOps). It simplifies dataset management, model training, and AI model operation, streamlining the end-to-end process for developers. A cloud-based Integrated Development Environment (IDE) supports seamless DNN development, including thorough tools for data labelling, intuitive dataset management, experiment tracking, continuous training, and effortless model deployment. The platform is tailored to support the latest hardware including A100 GPU from Nvidia and MI200 GPU from AMD.
- Cloud IDE - Enables easy DNN model development and data preprocessing with infrastructure like JupyterLab, it supports hyperparameter tweaking, third-party IDE integration, tensorboard usage, and GPU monitoring.
- Advanced Data Labelling - Supports multiple annotation formats compatible with computer vision applications, natural language processing, audio/speech recognition, and time-series analysis.
- Training Experiment Tracking - Helps track important aspects of ML training including parameters, metrics, console logs, and logged files/data with visualization support using TensorBoard.
- Autonomy for Continuous Training - Users can define pipelines for common ML tasks and even design end-to-end pipelines supporting MLOps for iterative model training, evaluation, and deployment.
- Support for Latest Hardware - MLSteam provides readily available support for the latest hardware including Nvidia's A100 GPU and AMD's MI200 GPU, enabling easy and quick setup for DNN developers.
MLSteam AI Platform from Myelintek is a robust deep neural networks (DNN) lifecycle solution, encompassing development and deployment stages, with an integrated approach to Machine Learning Operations (MLOps). It simplifies dataset management, model training, and AI model operation, streamlining the end-to-end process for developers. A cloud-based Integrated Development Environment (IDE) supports seamless DNN development, including thorough tools for data labelling, intuitive dataset management, experiment tracking, continuous training, and effortless model deployment. The platform is tailored to support the latest hardware including A100 GPU from Nvidia and MI200 GPU from AMD.
- Cloud IDE - Enables easy DNN model development and data preprocessing with infrastructure like JupyterLab, it supports hyperparameter tweaking, third-party IDE integration, tensorboard usage, and GPU monitoring.
- Advanced Data Labelling - Supports multiple annotation formats compatible with computer vision applications, natural language processing, audio/speech recognition, and time-series analysis.
- Training Experiment Tracking - Helps track important aspects of ML training including parameters, metrics, console logs, and logged files/data with visualization support using TensorBoard.
- Autonomy for Continuous Training - Users can define pipelines for common ML tasks and even design end-to-end pipelines supporting MLOps for iterative model training, evaluation, and deployment.
- Support for Latest Hardware - MLSteam provides readily available support for the latest hardware including Nvidia's A100 GPU and AMD's MI200 GPU, enabling easy and quick setup for DNN developers.