VESSL
VESSL is an end-to-end ML/ MLOps platform that enables machine learning engineers (MLEs) to customize and execute scalable training, optimization, and infere...
Why Choose VESSL?
VESSL is the way to go if you’re tired of wrestling with complex ML infrastructure and want a platform that makes training and deploying machine learning models quick and painless. It’s perfect for teams of any size looking to speed up their ML workflows without the usual headaches.
VESSL is an end-to-end ML/ MLOps platform that enables machine learning engineers (MLEs) to customize and execute scalable training, optimization, and inference tasks in seconds. These individual tasks can then be pipelined using our workflow manager for CI/CD. We abstract the complex compute backends required to manage ML infrastructures and pipelines into an easy-to-use web interface and CLI, and thereby fasten the turnaround in training to deployment. Building, training, and deploying production machine learning models rely on complex compute backends and system details. This forces data scientists and ML researchers to spend most of their time battling engineering challenges and obscure infrastructure instead of leveraging their core competencies – developing state-of-the-art model architectures. Existing solutions like Kubeflow and Ray are still too low-level and require months of a complex setup by a dedicated system engineering team. Top ML teams at Uber, Deepmind, and Netflix have a dedicated team of MLOps engineers and an internal ML platform. However, most ML practitioners, even those at large SW companies like Yahoo, still rely on scrappy scripts and unmaintained YAML files and waste hours just to set up a dev environment. VESSL helps companies of any size and industry adopt scalable ML/ MLOps practices instantly. By eliminating the overheads in ML systems with VESSL, companies like Hyundai Motors, Samsung, and Cognex are productionizing end-to-end machine learning pipelines within a few hours.
VESSL Introduction
What is VESSL?
VESSL is an end-to-end ML/ MLOps platform that enables machine learning engineers (MLEs) to customize and execute scalable training, optimization, and inference tasks in seconds. These individual tasks can then be pipelined using our workflow manager for CI/CD. We abstract the complex compute backends required to manage ML infrastructures and pipelines into an easy-to-use web interface and CLI, and thereby fasten the turnaround in training to deployment. Building, training, and deploying production machine learning models rely on complex compute backends and system details. This forces data scientists and ML researchers to spend most of their time battling engineering challenges and obscure infrastructure instead of leveraging their core competencies – developing state-of-the-art model architectures. Existing solutions like Kubeflow and Ray are still too low-level and require months of a complex setup by a dedicated system engineering team. Top ML teams at Uber, Deepmind, and Netflix have a dedicated team of MLOps engineers and an internal ML platform. However, most ML practitioners, even those at large SW companies like Yahoo, still rely on scrappy scripts and unmaintained YAML files and waste hours just to set up a dev environment. VESSL helps companies of any size and industry adopt scalable ML/ MLOps practices instantly. By eliminating the overheads in ML systems with VESSL, companies like Hyundai Motors, Samsung, and Cognex are productionizing end-to-end machine learning pipelines within a few hours.
How to use VESSL?
Visit website for more info
Why Choose VESSL?
VESSL is the way to go if you’re tired of wrestling with complex ML infrastructure and want a platform that makes training and deploying machine learning models quick and painless. It’s perfect for teams of any size looking to speed up their ML workflows without the usual headaches.
VESSL Features
Features
Feature information not available.
Pricing
Pricing information not available
VESSL Tags
Information not available.