Data sciencE that delivers
Turn your AI/ML projects into real use-cases
Automate, optimize, and scale your Machine Learning processes with DKube. Build and deploy AI models into production faster, monitor and train them in real-time, with DKube.
Here's who we work with
HOW DKUBE CAN HELP YOU
Bring AI to where your data is
Data security is paramount today, and we understand this priority. DKube is a unique MLOps solution that can operate on-premise and on any cloud or multi-cloud. Bring your open-source AI tool sets to where your data is.
Monitor and manage the entire development lifecycle
Over 90% of ML models today do not become usable in the real world. Take your ML models into production and continuously track their performance over time with DKube.
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Work with the tool sets you already use
DKube built on open source foundations of Kubeflow and MLflow. It supports every open-source toolset your teams currently use and will continue to stay open-source forever.
Control processes better with an open-source-based MLOps platform
Bring the efficiency of DevOps to your AI/ML projects at a fraction of the cost of investing in proprietary tools. Gain full control over your project lifecycle with a purpose-built platform.
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Customize the DKube solution for evolving needs
Most open-source MLOps solutions, if assembled and supported in-house, can get expensive to host and maintain over time. This is precisely why we’ve built the DKube Lite and DKube Enterprise
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Get all your teams on the
same page
Data Engineers, Data Scientists, IT Teams, and Business Executives love DKube! We’ve built features that support every relevant role and function within your organization.
BENEFITS
DKube is built to be used by everyone on the team
DKube is how data engineers, data scientists, production teams, and business leaders communicate. Packed with a ton of features, DKube allows you to set the right business goals, monitor progress, and build and deploy AI models.
Data Engineers
With automated model deployment, streamlined data, and ML pipelines, let DKube streamline your data engineering workflow
Production Teams
When the rubber hits the road, we’re ready for you! DKube supports seamless collaboration between teams and helps you successfully take ML models into production, monitor their journey, and report.
Data Scientists
Set the direction for how your work is taken into production, and collaborate with every team that contributes to your workflow. DKube works with every toolset that you currently use.
IT Managers
We help you get the fundamentals absolutely perfect. Optimize the cost of hardware by laying a strong foundation for the tools and technology that will eventually be used by your AI teams,
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Business Leaders
Enhance the productivity of AI/ML teams and projects, monitor progress through business dashboards, and build a compelling use casefor using AI for your business.
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Strategy
The most revolutionaryAI model may be stuck on a shelf right now
According to the McKinsey State of AI 2021 report, 7 in 10 ML models never become business use-cases. Moving from research to production can be daunting- continuous training, serving and monitoring is the need of the hour.
For that to happen, you need truly collaborative teams. We’ve built DKube to help you achieve just that.
With a purpose-built tool that helps manage the entire MLOps lifecycle, you can achieve
10X
Reduction in production time
45%
Reduction in costs
38%
Better production outcomes
Blog
The ExtraordinarAI Resource Center
Testimonials
Here's what customers
have to say about us
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FAQ
Build, train, and deploy AI models faster
Is DKube restricted to on-prem use cases?
DKube can be used in both single cloud and multi tenant use cases just as seamlessly. On-premise deployment is usually most suited for companies that have stringent restrictions on where they store and process their data. Even in such circumstance, DKube is able to offer you all of the MLOps capabilities you need.
At what stage of MLOps maturity would I need a platform like DKube?
Organizations at all stages of MLOps maturity can achieve quantifiable benefits from using DKube. We have both individual components and end-to-end solutions to enable you to achieve more with your AI/ML projects in a shorter amount of time. Please take a look at the Products pages to know more and find your best-fit solution.
How long does it take to set up my first project on DKube?
Once DKube is installed, your first project can be ready within the hour. Installation itself typically takes a few days to plan, and one day for execution.
We already use several ML tools and frameworks. Can they be used with DKube?
DKube uses best-in-class frameworks and tools, including TensorFlow, PyTorch, Scikit Learn, Keras, JupyterLab, RStudio, and Katib.
DKube supports the most common authorization standards: GitHub & LDAP, the most popular code repositories including GitHub, GitLab, and Bitbucket, as well as common storage standards for data and models, including GitHub, GitLab, Bitbucket, AWS S3, Minio, Google Cloud Storage, and Redshift. If you don't see a tool or framework you work with here, please reach out to us.
Why do I need an MLOps platform like DKube?
To train, deploy, and monitor models at scale, consistency is key. MLOps tools like DKube help you collaborate better with the many teams that are often involved in taking a research concept and bringing it to life. The team behind DKube has consulted on several AI/ML projects, and DKube was built to solve the most common issues that plague development. For details or to schedule a demo, please contact us.
Get started with DKube
There's no such thing as an average AI project, and they could all benefit from speed and efficiency. Find out how.
