Machine learning
lifecycle management
Cubonacci enables organizations to focus on developing custom machine learning models without having to worry about peripheral matters. The Cubonacci platform manages deployment, versioning, infrastructure, monitoring and lineage for you, eliminating risk and minimizing time-to-market.
Train, deploy and maintain in no time
Developing a machine learning model is an iterative research process that takes time. Due to complexity in industrializing these models, deployment is usually done only after a number of iterations.
Cubonacci minimizes the time between development and deployment by adopting a DevOps mindset to machine learning.
Discover the added value of Cubonacci for you
Cubonacci for leaders
Our platform provides leaders with peace of mind that data scientists are focusing on their core skills, that costs associated with training and running models is minimized and that model health is continuously monitored.
Cubonacci leverages state of the art cloud technology with best practice DevOps methodologies to allow data scientists on adding value instead of wasting time on issues that are already solved in Cubonacci.
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Cubonacci for data scientists
Our platform allows data scientists to focus on what really matters. Many of the engineering tasks required to manage the full machine learning lifecycle are solved.
Cubonacci manages the infrastructure, the flow between the different part of the lifecycle of the models and testing plus monitoring of the machine learning solutions. Auto scaling everything while remaining flexible in how data scientists solve their use case specific problems.
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Cubonacci for engineers
Our platform allows engineers involved in both the data input and the downstream systems using the predictions to integrate easily. Cubonacci provides easy to use APIs with generated code examples to help automation. Continuous monitoring on both input and output means that stakeholders are immediately notified in the case of issues.
By being agnostic to the data environment, Cubonacci operates everywhere. Models deployed as batch process can be scheduled, models deployed as API are approachable via REST and gRPC and models deployed as a stream can connect to many queues.
Engineers are supported by easy integration both on the automation and the data side. Continuous monitoring gives peace of mind to all the stakeholders involved.
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Discover the added value of Cubonacci for you
Cubonacci for leaders
Our platform provides leaders with peace of mind that data scientists are focusing on their core skills, that costs associated with training and running models is minimized and that model health is continuously monitored.
Cubonacci leverages state of the art cloud technology with best practice DevOps methodologies to allow data scientists on adding value instead of wasting time on issues that are already solved in Cubonacci.
Features
- Minimize time-to-market
- Complete overview of system
- Reduce compute costs of training and deployment
- Model performance monitoring
- Smart alerting
- Compliance
Cubonacci for data scientists
Our platform allows data scientists to focus on what really matters. Many of the engineering tasks required to manage the full machine learning lifecycle are solved.
Cubonacci manages the infrastructure, the flow between the different part of the lifecycle of the models and testing plus monitoring of the machine learning solutions. Auto scaling everything while remaining flexible in how data scientists solve their use case specific problems.
Features
- Batch, streaming and API deployment
- Distributed hyperparameter search
- A/B testing and multi-arm-bandit
- Seamless handover to deployment
- Scheduled retraining
- Python and R runtimes supported
- Smart alerting
Cubonacci for engineers
Our platform allows engineers involved in both the data input and the downstream systems using the predictions to integrate easily. Cubonacci provides easy to use APIs with generated code examples to help automation. Continuous monitoring on both input and output means that stakeholders are immediately notified in the case of issues.
By being agnostic to the data environment, Cubonacci operates everywhere. Models deployed as batch process can be scheduled, models deployed as API are approachable via REST and gRPC and models deployed as a stream can connect to many queues.
Engineers are supported by easy integration both on the automation and the data side. Continuous monitoring gives peace of mind to all the stakeholders involved.
Features
- Runs on any Kubernetes cluster, in the cloud or on premise
- Automated horizontal and vertical scaling
- Fully features rollback functionality
- Role based access control
- API for automation
- Easy integration
- Smart alerting
Workflow
Model available for scalable experimenting
