MLOps Driven Machine Learning on Kubernetes
Empowering organizations to rapidly turn data into actionable insight by democratizing machine learning and automating complex workflows.
The complexity of data science, cloud native infrastructure, frameworks, patterns, and data is ever increasing.
Organizations want to achieve a continuous deployment model for data science just as traditional DevOps has achieved, but there are significant challenges;
- Fragmented environments: A Data Scientist’s local environment operates very differently to a production cloud environment
- Disjointed workflows: Data Scientists and Machine Learning Engineers want to work in Jupyter Notebooks, VSCode, and other familiar tools. They are data science experts, not infrastructure or storage specialists
- Data versioning hell: Machine learning is unique – data, as well as code, is constantly changing, making debugging and collaboration nearly impossible
Reality Of Most Data Science Projects
Months to Production
Arrikto’s Machine Learning solutions enable data science and machine learning operations teams to collaborate together to continuously build, train, deploy, and serve machine learning models with DevOps efficiency.
Common Integrated Foundation
Go faster with a standardized and consistent environment from your laptop to the cloud with all of your familiar tools, frameworks, and libraries.
Integrated Workflow Automation
Reduce complexity and reliance on DevOps Engineers with push-button code to pipeline and hyperparameter automation. Focus on models, not Docker containers and cloud infrastructure.
Compliant Reproducible Pipelines
Train and debug models collaboratively even as underlying models, code, and data change. Keep track of experiments,
Making Democratized Intelligence Automation The Reality For Everyone