talk2us@mlone.ai
MLOne Platform
NoOps Machine Learning
is Here
is Here
Build in Jupyter, deploy without changing code
MLOne Platform
NoOps Machine Learning is Here
Build in Jupyter, deploy without changing code
Watch Our Demo
Automate!
MLOne is a cloud-based platform that enables to build, deploy and manage machine learning models without boilerplate code.
We have fully automated DevOps process so you do not have to spend time on it.
Focus on building and improving models rather than
worrying about the environment setup, containerization
or modification code for production
End-to-End MLOne Process
MLOne reshapes the way machine learning models
are deployed and managed in production
are deployed and managed in production
1
Train
Utilize a wide range of hardware configurations for training or register
your production ready model
effortlessly with one click
your production ready model
effortlessly with one click
2
Deploy
Quickly and easily deploy your models
in flexible, auto-scalable and reliable
cloud without changing a single
line of code from a Jupyter notebook
in flexible, auto-scalable and reliable
cloud without changing a single
line of code from a Jupyter notebook
3
Monitor
Use visualization tools and a simple
python API to monitor productivity
of ML services or automatically detect
data and concept drifts
python API to monitor productivity
of ML services or automatically detect
data and concept drifts
1
Train
Utilize a wide range of hardware configurations for training or register your production ready model effortlessly with one click
2
Deploy
Quickly and easily deploy your models in flexible, auto-scalable and reliable cloud without changing a single line of code from a Jupyter notebook
3
Monitor
Use visualization tools and a simple python API to monitor productivity of ML services or automatically detect data and concept drifts
Register & Train
Handle your model effortlessly with one click
Upload your production ready
models using simple API, or train
models with MLOne
Train multiple models
at the same time
Distribute training of large
generative models using multiple
GPU instances
Easily provide your team
with a wide range of multiple GPUs hardware configurations
Organize versioning of data,
code and parameters
for every ML build
Ensure your models are well
organized and easy to manage
with the 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
Tags
Deploy a model in minutes,
not weeks
not weeks
Tilda Publishing
In everyday life, you often have to extract code from a Jupyter Notebook and refactor it, as well as create scripts before it can be deployed on a server.
With the help of our tags, you can use the code as is. Just place them in the right locations, and the code will automatically come together into a full-fledged application.
How do tags work?
Simply write the inference code for your model in a Jupyter cell, tag it with a special marker, and let MLOne automatically create an auto-scalable production service in the cloud.
Monitor
Monitor the performance of deployed models.
Detect concept and data drifts in a timely manner
Detect concept and data drifts in a timely manner
- 1Track models metrics, service performance, and resources usage with powerful visualization tools
- 2Compare the baseline and the inference data to make sure model actuality
- 3Automatically detect data and concept drifts to manage model lifecycle
- 4Quickly react to customizable alerts when thresholds are exceeded
Why MLOne?
Fully 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 private Large Language Models
- Customize your environment with a wide range of powerful instances in a few clicks
- No need to edit YAML files or manually configure infrastructure
- Neither containers nor images needed
- Train and deploy on the AWS cluster without learning tons of documentation
Increase Data Science Team Performance
No Timespend on DevOps100% time spent on building models
Quick time-to-marketUp to 4 months faster from research to production
High performance94 models out of 100 make it to production
Increase Data Science Team Performance
- No Timespend on DevOps100% time spent on building models
- Quick time-to-marketUp to 4 months faster from research to production
- High performance94 models out of 100 make it to production
Up to
upfront investments
10
lower
Pricing
Pay only for what you use
Trial
- -Jupyter Notebooks for code development
- - An innovative tagging system
- - Multiple AWS instances for training and deployment
- - Top-of-the-line hardware with GPUs and CPUs
- - Documentation and quick-start examples
- - MLflow for model logging
- - A scalable and reliable cluster
- - Monitoring tools: Grafana, Prometheus, Alertmanager
- - Scheduling services to save money
- - Clear spending statistics
- - $100 AWS instances usage limit is included
Try for Free
Standard
- - Jupyter Notebooks for code development
- - An innovative tagging system
- - Multiple AWS instances for training and deployment
- - Top-of-the-line hardware with GPUs and CPUs
- - Documentation and quick-start examples
- - MLflow for model logging
- - A scalable and reliable cluster
- - Monitoring tools: Grafana, Prometheus, Alertmanager
- - Scheduling services to save money
- - Clear spending statistics
- - 24/7 Support
Contact Sales
Pricing
Pay only for what you use
Trial
- -Jupyter Notebooks for code development
- - An innovative tagging system
- - Multiple AWS instances for training and deployment
- - Top-of-the-line hardware with GPUs and CPUs
- - Documentation and quick-start examples
- - MLflow for model logging
- - A scalable and reliable cluster
- - Monitoring tools: Grafana, Prometheus, Alertmanager
- - Scheduling services to save money
- - Clear spending statistics
- - $100 AWS instances usage limit is included
Try for Free
Standard
- - Jupyter Notebooks for code development
- - An innovative tagging system
- - Multiple AWS instances for training and deployment
- - Top-of-the-line hardware with GPUs and CPUs
- - Documentation and quick-start examples
- - MLflow for model logging
- - A scalable and reliable cluster
- - Monitoring tools: Grafana, Prometheus, Alertmanager
- - Scheduling services to save money
- - Clear spending statistics
- - 24/7 Support
Contact Sales
Try For Free
Request your free trial. No credit card needed
Try For Free
Request your free trial. No credit card needed