The Scalable MLOps Platform
Enabling CI/CD for ML and pipeline automation on-prem and any-cloud
The Valohai MLOps platform helps you streamline complex ML workflows with:
- Framework agnostic ML, on-premises and single/multi/hybrid-cloud
- Automatic versioning with complete lineage of your ML experiments
Hybrid and Multi-Cloud Support
Manage AI workloads across multiple clouds and on-premises data centers with ease.
Scalability and Performance
Scale your Machine Learning Operations efficiently and optimize model performance for better outcomes.
Streamlined Collaboration
Facilitate cross-functional collaboration between data scientists, IT, and business units to drive AI initiatives.
The MLOps platform purpose-built for ML Pioneers
Valohai is the first and only cloud-agnostic, MLOps platform that ensures end-to-end automation and reproducibility. Think CI/CD for ML.
Knowledge repository
Store and share the entire model lifecycle
Collaborate on anything from models, datasets and metrics.
With Valohai, you can:
- Automatically version every run to preserve a full timeline of your work.
- Compare metrics over different runs and ensure you & your team are making progress.
- Curate and version datasets without duplicating data.
Smart orchestration
Run ML workloads on any hybrid multicloud with a single click
Execute anything on any infrastructure with a single click, command or API call.
With Valohai, you can:
- Execute anything on any infrastructure with a single click, command or API call.
- Orchestrate ML workloads on any cloud or on-premise machines.
- Deploy models for batch and real-time inference, and continuously track the metrics you need.
Developer core
Build with total freedom and use any libraries you want
Your code, your way. Any language or framework is welcome.
With Valohai, you can:
- Turn your scripts into an ML powerhouse with a few simple lines.
- Develop in any language and use any external libraries you need.
- Integrate into any existing systems such as CI/CD using our API and webhooks.
- I/O
- Parameters
- Metadata
data = pd.read_csv(valohai.inputs("dataset").path(), header=0)
output_path = valohai.outputs("model").path(filename='iris.hdf5')
model.save(output_path, include_optimizer=False)
And many more!
Ready integrations make life easy
Hugging Face 
Super Gradients 
Snowflake 
Redshift 
BigQuery 
V7 Labs 
Labelbox 
Docker 
Spark Can't find what you're looking for?
Don't worry! Valohai can run any code so you're never limited to just out-of-the-box integrations.
See how the pioneers do it
Start building. Stop managing.
Start building. Stop managing.
“Valohai allows us to scale up machine learning without worrying about managing infrastructure. The platform has drastically changed how we build our team because our expertise can be more focused on data science and less on cloud and DevOps. All in all, Valohai accelerates how quickly we can develop and launch solutions while keeping our costs down.”
Experiment at scale without worry.
Experiment at scale without worry.
“Large-scale experimentation tends to be tricky because you’ll need to manage cloud resources, and mistakes can be quite costly. With Valohai, though, that stress is gone, and we can focus on the actual data science. The version control of all parts of an experiment, from code to data to environment, allows for systematic research, which can be reviewed months later.”
Skip ahead with managed MLOps.
Skip ahead with managed MLOps.
“Building a barebones infrastructure layer for our use case would have taken months, and that would just have been the beginning. The challenge is that with a self-managed platform, you need to build and maintain every new feature, while with Valohai, they come included.”
