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THE SCALABLE MLOPS PLATFORM
ENABLING CI/CD FOR ML AND PIPELINE AUTOMATION ON-PREM AND ANY-CLOUD
Multi-CloudCloud AgnosticAI InfrastructureHybrid CloudCI/CD PipelinesModel
ManagementLLMDeep LearningScalable MLMLOpsAny CloudData Science
On-Premises MLML Experiment TrackingML OrchestrationAI AutomationReproducible
MLPrivate CloudML OperationsCollaborative MLModel RetrainingIn-house
infrastructureMachine Learning
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
Join the ML Pioneers shaping the world
Start for freeBook a custom demo
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)
Work seamlessly across all environments
And many more!
READY INTEGRATIONS MAKE LIFE EASY
ModelNLP
Hugging Face
ModelCV
Super Gradients
DataStructured
Snowflake
DataStructured
Redshift
DataStructured
BigQuery
DataUnstructured
V7 Labs
DataUnstructured
Labelbox
Other
Docker
Other
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.
Get Started
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.”
Petr JordanCTO @ Onc.ai
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.”
Andres HernandezLead Data Scientist @ KONUX
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.”
Renaud AlliouxCTO @ Preligens
SCALE ML DEVELOPMENT
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