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.

Turn your AI/ML projects into
Real Use Cases

Automate, optimize, and scale your machine learning workflows with DKube – build, deploy, monitor, and continuously train production-ready AI models faster and at enterprise scale.

Discover DKube

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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.

Work with the tool sets you already use
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DKube is 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.

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.

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.

Data Engineers

With automated model deployment, streamlined data, and ML pipelines, let DKube streamline your data engineering workflow.

EXPLORE THE MODULE

Accurate, consistent data

Clean, transform, and prep data so it’s ready for AI/ML workflows.

Feature Engineering

End-to-end data prep with Feature Store support and workflow flexibility.

Feature Sets

Versioned, reusable datasets with full lineage tracking for collaboration.

MLOps on HPC (Slurm & LSF)

Seamlessly run AI workloads across Slurm and LSF with full DKube MLOps support.

Hub & Spoke Architecture

Hybrid management for Kubernetes and remote compute systems.

Private Endpoints

Simplified APIs to effortlessly deploy private and protected endpoints for enterprises within your private VPC or on-premise

EXPLORE THE MODULE

Model Deployment

Turn trained models into scalable, secure web services or APIs without managing infrastructure.

Infrastructure Automation

Automate workflows with native pipeline support and Git-triggered runs.

Kubeflow-Native Serving

Deploy production models using the standard Kubeflow serving framework for consistency and compatibility.

Pre/Post-Processing Support

Include preprocessing and postprocessing logic in served images for cleaner, ready-to-use model endpoints.

Monitoring & Version Control

Track deployed endpoints and swap model versions easily with dashboard-driven monitoring and management.

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.

EXPLORE THE MODULE

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.

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Metric Collection & Management

Automatically collect, store, and compare key training metrics in real time throughout the model lifecycle.

Model Tracking & Lineage

Track model performance, history, and lineage for deeper insights and better decision-making.

Hyperparameter Optimization

Tune and optimize model hyperparameters seamlessly as part of your experiments.

Custom IDE Images

Run data science workflows in familiar environments by using custom notebook images and tools.

Collaboration & Unified Workflow

Collaborate across teams with shared tools and datasets, aligning science and outcomes in one platform.

Model Performance & Reliability

Continuously monitor deployed models to ensure accuracy, stability, and business goal alignment over time.

Integrated Monitoring Dashboard

View all monitored models in one place and quickly assess performance trends and alerts.

Root Cause Analysis

Drill down into data drift, deviations, and performance issues to identify what’s impacting model quality.

Retraining Workflow Support

Use integrated tracking and lineage to retrain models efficiently when performance degrades.

Seamless Redeployment

After improving a model, push updated versions and carry monitoring settings forward easily.

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.

EXPLORE THE MODULE

Business Leaders

Enhance the productivity of AI/ML teams and projects, monitor progress through business dashboards, and build a compelling use case for using AI for your business.

EXPLORE THE MODULE

End-to-End Workflow Support

Run the full MLOps lifecycle — from data prep and feature engineering to deployment and monitoring — in one integrated workflow.

Tracking & Lineage

Capture detailed lineage and metadata so experiments, models, and outcomes are fully traceable and reproducible.

Rich Metric Management

Collect, compare, and visualize ML metrics with MLFlow-based support throughout your model development.

Flexible Integrations

Plug into popular code repos, storage systems, and data sources for seamless collaboration and pipeline automation.

CI/CD Automation

Automate build, test, and deployment processes so teams can ship models faster with repeatable quality.

Got questions?
We’ve got answers

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.

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Turn your AI/ML projects into
Real Use Cases

Bring AI to where your data is

Monitor and manage the entire development lifecycle
‍

Work with the tool sets you already use
‍

Control processes better with an open-source-based MLOps platform