Digazu’s MLOps platform optimizes the ML lifecycle from development to production. It offers a flexible solution for data scientists, with accelerators to start exploring data and building models quickly while implementing best practices.
Empower and enhance your data scientists productivity and effectiveness. Once the development is completed, the deployment of the model training and model serving pipelines is easily automated. Use the MLOps’s sharing and collaborative features to make it easy for your teams to learn from each other. The gained expertise enhances the effectiveness and robustness of their predictive models to create added-value.
Shorten the deployment times of your data projects and increase the number of your go-to-market data projects. Thanks to our state-of-the-art technology, you deploy a production-ready data science model in 1 click.
Give data scientists the flexibility they need to use the infrastructure and the technologies they need to work efficiently and provide production-ready results quickly.
Give data scientists the flexibility they need to use the infrastructure and the technologies they need to work efficiently and provide production-ready results quickly.
Benefit from our platform’s high robustness, performance, availability, and scalability thanks to the state-of-the-art technologies, carefully tested and selected by our private research center.
By using our standard way of working, you reduce maintenance and dependence on your developers and apply DevOps principles on your ML projects. All your models are handled the same way, making it easy for newcomers to adapt and improve them.
Benefit from a packaged notebook server to start data exploration and data science experimentation directly from your web-browser, while having the flexibility to make use of many AI/ML technologies. Use our best practices and templates to ensure a smooth and automated transition from exploration to production.
Use Digazu’s MLOps platform to exchange ideas and to provide data scientists with a collaborative environment, complemented by visualizations, where they can share their results, given that their results stem from the fact that they have expertise in different techniques and technologies. Use our best practices to share codes in a standardized way, facilitate knowledge transfer, and reduce maintenance costs.
Integrate the MLOps platform with your version control system and with your CI/CD to implement DevOps principles and automate the deployment of your models into production. Benefit from state-of-the-art technology for running your models in a distributed and scalable environment.
Use the platform’s UI to configure your data science pipelines in production. Train and retrain your models whenever you want, or schedule them automatically. After validating your models on the basis of custom metrics, redeploy the latest version in one click. Choose to expose your models as a REST API or to execute them regularly on your latest dataset.
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