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MLOne
Cloud-based ML Platform
No DevOps Needed
No DevOps Needed
MLOne is a cloud-based platform that enables to build, deploy and manage machine learning models right from Jupyter without boilerplate code.
It allows users to focus on building and
improving their models rather than worrying about the environment setup, containerization or modification code for production.
It allows users to focus on building and
improving their models rather than worrying about the environment setup, containerization or modification code for production.
MLOne
Cloud-based ML Platform
No DevOps Needed
No DevOps Needed
MLOne is a cloud-based platform that enables to build, deploy and manage machine learning models right from Jupyter without boilerplate code.
It allows users to focus on building and
improving their models rather than worrying about the environment setup, containerization or modification code for production.
It allows users to focus on building and
improving their models rather than worrying about the environment setup, containerization or modification code for production.
Main Features
Stay in your comfort zone: do everything in Jupyter notebook!
Use unique tagging system instead of changing code
Use unique tagging system instead of changing code
TrainUtilize a wide range of hardware configurations for training or register your production ready model effortlessly with one click
DeployQuickly and easily deploy your models in flexible, auto-scalable and reliable cloud from a Jupyter notebook
MonitorUse visualization tools and a simple python API to monitor productivity of ML services or automatically detect data and concept drifts
Automate!
MLOne is designed to simplify the process of deploying
and operating machine learning models
and operating machine learning models
Automate!
MLOne is designed to simplify the process of deploying and operating machine learning models
Register & Train
Handle your model effortlessly with one click
Upload or TrainUpload your production ready
models using simple API or train
models with MLOne
Train in ParallelTrain multiple models
simultaneously
Distribute trainingDistribute training of large
generative models using multiple
GPU instances
Set ResourcesEasily provide a wide range of
hardware configurations with GPUs/CPUs to your team
Organaze VersioningOrganize versioning of data,
code and parameters for every
ML build
Manage ModelsEnsure your models are well-
organized and easy to manage with
a model registry
Deploy
Quickly and easily deploy online and batch
ML models into production
ML models into production
With a few clicks build a fault tolerant, highly available and reliable cloud based production environment
Serve models on the independent instances or on the elastic cluster
Build auto-scalable ML-services directly from Jupyter
Use your favorite ML stack: Pytorch, Tensorflow, Keras, MLFlow, SKlearn, Spark-mlib, triton, onnx and many others- Schedule running of your services to reduce the costs
- Use secured inference endpoints out of the box
Deploy
Quickly and easily deploy
online and batch ML models into production
online and batch ML models into production
- With a few clicks build a fault tolerant, highly available and reliable cloud based production environment
- Serve models on the independent instances or on the elastic cluster
- Build auto-scalable ML-services directly from Jupyter
- Use your favorite ML stack: Pytorch, Tensorflow, Keras, MLFlow, SKlearn, Spark-mlib, triton, onnx and many others
- Schedule running of your services to reduce the costs
- Use secured inference endpoints out of the box
Monitor
Monitor the performance of deployed models. Detect concept and data drifts in a timely manner
Track models metrics, services performance and resources
utilization using the powerful visualization tools
Compare baseline and inference data to ensure model actuality
Automatically detect data and concept drifts to the manage
the lifecycle of the model- React quickly to configurable alerts when thresholds exceeded
Monitor
Monitor the performance of deployed models. Detect concept and data drifts in a timely manner
Track models metrics, services performance and resources
utilization using the powerful visualization tools
Compare baseline and inference data to ensure model actuality
Automatically detect data and concept drifts to the manage
the lifecycle of the model- React quickly to configurable alerts when thresholds exceeded
Why MLOne?
Completely automated development stage:
one tool to rule them all
one tool to rule them all
- Unique built-in tagging system to escape boilerplate code
- Native support for the private Large Language Models
- Customization of the environment in a few clicks
- No need to edit YAML files or manually configure infrastructure
- Neither containers nor images needed
- No need to learn tons of documentation to deploy on the AWS cluster
Why MLOne?
Completely automated development stage: one tool to rule them all
- Unique built-in tagging system to escape boilerplate code
- Native support for the private Large Language Models
- Customization of the environment in a few clicks
- No need to edit YAML files or manually configure infrastructure
- Neither containers nor images needed
- No need to learn tons of documentation to deploy on the AWS cluster
Ready to try MLOne?
Try For Free
Request your free trial. No credit card needed
Try For Free
Request your free trial. No credit card needed