Assembling (or rather gluing) multiple data tools together and hoping they will work seamlessly can be a painful experience. That’s why we put all the steps of your machine learning workflow onto a single platform.
Extract data from various sources into a single relational or object storage using a vast choice of connectors.
Develop and automate transformation pipelines using your preferred language (SQL, R, Python, Spark, Julia, and others).
Prototyping and development
Experiment with your data and develop collaborative workspaces and machine learning models.
Bringing it to production
Models add value only when used in production. Deploy and serve your models right after you train (and test) them.
Gain the value
Build and automate a pipeline for batch inference using the deployed models or use a model from another application via the RESTful API.
An environment created by data scientists for data scientists
Use what you know and love
Whether you prefer SQL (Snowflake, Redshift, Synapse, etc.), Python, Spark (JupyterLab), or R (RStudio), we support it. Choose whatever language you like, or a combination of languages if you need to.
Don’t be limited
Are you about to build something huge? Just select the performance of your workspace from the expandable list of options. Are you using a machine with GPUs? Select them and go.
Track your experiments
Track every run of your experiments and log all the parameters, metrics, notes, and artifacts thanks to Keboola’s MLflow integration. You can also utilize JupyterLab’s GIT integration to work comfortably with external repositories.
Pay only for what you consume
Create, pause, restore, and repeat. Put your data science workspace into sleep mode or rely on our auto-sleep feature to ensure that you’re not paying for it when you’re not using it. Deployed models can be also easily stopped and redeployed, and you pay only for the time the model was LIVE.
Bring it to production
Machine learning development is rarely a one-person show. Utilize our shareable workspaces to collaborate on experiments with your colleagues.
If you don't like JupyterLab, then just use GIT.
Deploy to production
Deployment to production can be a heavy task. It involves testing the performance of a model, running multiple models in parallel for some time, and then testing again.
Trained models can easily be deployed and then managed via a UI and an API. All models are available to you via the RESTful API 24/7.
Using the deployed model
Models can be used either from Keboola Transformations within your automated batch pipeline or from external applications via the API.
Overview of deployed models
You can easily see your models, their versions, descriptions, and even which transformation they are connected to.
Focus on your use cases, and let us handle the infrastructure
Data and business teams always know what could be optimized, what insights could be investigated, and even know what data they need. However, often, they simply don’t have any easy-to-use tools to do these tasks.
Don’t worry about the maintenance and operations as you can let us handle everything for you. Just focus on doing what you do best!
Powered by market-leading technologies
Docker, Kubernetes, MLflow, JupyterLab, Spark, Python, R, etc. We know what data scientists love, and we make these tools easily accessible for you.
Ready to get started? Request a free demo or get in touch, today.