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Quickly Integrate
ML Models to Improve Business Processes
ML Models to Improve Business Processes
Deploy, run and monitor ML models without DevOps expertise
MLOne
Cloud-based ML Platform
No DevOps Needed
No DevOps Needed
MLOne is a cloud-based software-as-a-service (SaaS) platform that enables small and medium-sized enterprises (SME) to deploy and operate machine learning models quickly and easily.
The platform allows to upload pre-trained models, and MLOne takes care of the deployment, scaling, and management of the models in the cloud.
The platform allows to upload pre-trained models, and MLOne takes care of the deployment, scaling, and management of the models in the cloud.
MLOne
Cloud-based ML Platform
No DevOps Needed
No DevOps Needed
MLOne is a cloud-based software-as-a-service (SaaS) platform that enables small and medium-sized enterprises (SME) to deploy and operate machine learning models quickly and easily.
The platform allows to upload pre-trained models, and MLOne takes care of the deployment, scaling, and management of the models in the cloud.
The platform allows to upload pre-trained models, and MLOne takes care of the deployment, scaling, and management of the models in the cloud.
Simplify!
MLOne is designed to simplify the process of deploying
and operating machine learning models.
It allows users to focus on building and improving their models rather than the complexities of managing the underlying infrastructure.
Register & Train
Register your model effortlessly with one click
Uplode or TrainUpload your production
ready models using simple API or train models with MLOne
Set ResourcesEasily provide multi GPU/CPU resources to data scientists for training
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
- Uplode or TrainUpload your production
ready models using simple API or train models with MLOne - Set ResourcesEasily provide multi GPU/CPU resources to data scientists for training
- 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
Use your favorite model formats: MLFlow, SKLearn, Spark-mlib, triton, tensorflow, onnx, etc.
With just one line of Python code, build fault-tolerant, highly available and automatically scalable ML-services
Easily design and build and orchestrate ML-model ensembles
Get neural network-based services optimized for fast inference
Deploy
Quickly and easily deploy
online and batch ML models into production
online and batch ML models into production
- Use your favorite model formats: MLFlow, SKLearn, Spark-mlib, triton, tensorflow, onnx, etc.
- With just one line of Python code, build fault-tolerant, highly available and automatically scalable ML-services
- Easily design and build and orchestrate ML-model ensembles
- Get neural network-based services optimized for fast inference
Monitor
Monitor the performance of deployed models. Detect concept and data drifts in a timely manner
Store and visualize performance metrics of the model
Utilize automated monitoring and visualization tools
to identify data drifts and outliers
Evaluate model performance to identify opportunities
for retraining to increase lifetime of ML-models
Monitor
Monitor the performance of deployed models. Detect concept and data drifts in a timely manner
Store and visualize performance metrics of the model
Utilize automated monitoring and visualization tools
to identify data drifts and outliers
Evaluate model performance to identify opportunities
for retraining to increase lifetime of ML-models
Operate
Operate ML-models using flexible, scalable
and reliable cloud infrastructure
and reliable cloud infrastructure
- Run your ML-models in production on a scalable cloud-based cluster
- Track resources utilization, services performance, system infrastructure and logs
- React quickly to alerts triggered when specific thresholds are exceeded
Operate
Operate ML-models
using flexible, scalable and reliable cloud infrastructure
using flexible, scalable and reliable cloud infrastructure
- Run your ML-models in production on a scalable cloud-based cluster
- Track resources utilization, services performance, system infrastructure and logs
- React quickly to alerts triggered when specific thresholds are exceeded
Set Infrastructure
Benefits for
Data Scientists
Data Scientists
Run training tasks in parallel
with different hyperparameters
for fast grid search
Train and deploy in Jupyter Notebook with no DevOps code using
the unique tagging system
Seamlessly incorporate
ChatGPT into your projects
Code
Debugging
Debugging
Code
Refactoring
Refactoring
Perfomance
Analysis
Analysis
Containerization,
Environment Setup
Environment Setup
Model
Deployment
Deployment
Monitoring
Serving
Visualization
DEV
OPS
managed
MLOne automated
on schedule
S3/
Data Analysis
Data Analysis
Model
Development
Development
Frameworks
Model
Lifecycle
Lifecycle
ML
Advantages for Business
- -1-Manage ExpencesScale ML models and avoid
paying for idle hardware
with pay-as-you-go pricing - -2-Reduce CostsMinimize investments
in employees and infrastructure required to start using models in your business processes - -3-Increase performanceIncrease DS team performance
and accelerate time-to-market
for ML-models
Ready to try MLOne?
Try For Free
Request your free trial to evaluate all the advantages
of the MLOne platform
of the MLOne platform
You agree to our Terms and Conditions
Try For Free
Request your free trial to evaluate all the advantages
of the MLOne platform
of the MLOne platform
You agree to our Terms and Conditions