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Run complex AI and ML workloads on the D2iQ Kubernetes Platform: DKP 2.0
D2iQ Kaptain: The end-to-end platform for ML on Kubernetes, to speed and simplify model deployment and reduce complexity for data scientists and platform teams alike

Why D2iQ Kaptain?

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Go From Prototype to Production in Minutes Instead of Months

Develop and Deploy Al and ML Workloads at Scale

Going from prototype to production is no easy task. Few Kubernetes distributions offer a well-rounded machine learning platform that is ready for production from the installation onward. D2iQ Kaptain is the only Kubeflow-based platform that provides a seamless Python-native user experience across training, tuning, deploying, and tracking of models, so you can iterate faster and more often. It comes with a comprehensive ML toolkit, including Jupyter Notebooks, that is pre-installed with the best frameworks, tools, and libraries with out-of-the-box GPU support. In addition, customers can also install their own packages with ease.
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Effective Collaboration Across Every Infrastructure

Enterprise Security and Multi-tenancy

While Kubeflow comes with many components to help you in your ML journey, it still lacks full fledged integration with multi-user isolation. Most enterprises lack the technical skills to deploy and configure these tools, especially in highly restrictive environments that are complex and costly to maintain. D2iQ Kaptain has made Kubeflow ready for the enterprise—install, run, and manage entire ML pipelines in the cloud, on-premise, and in air-gapped environments. ML teams can access shared compute resources (GPUs) in their own isolated workspace, and scale environments, add more users, or infrastructure resources as deployments grow.
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Unlock the Power of ML on Kubernetes with the Kaptain SDK

No Kubernetes Knowledge, No Problem. Train, Tune, and Deploy Models Without Leaving Your Notebook

A highly automated and integrated Kubeflow solution can deliver on the critical needs to manage the lifecycle of machine learning models, however it requires data scientists to learn Kubernetes. The D2iQ Kaptain SDK allows data scientists to focus on what matters to them: train, tune, and deploy models from within a single notebook without the need to switch contexts or learn additional technologies. Data scientists can train models on CPUs, GPUs, experiment in parallel, and deploy autoscaling services using only one-liners in Python.
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Ready for Day 2 Production

Accelerate Time to Value, Save on Production Costs

Security, cost management, and observability are essential capabilities that enterprises require in a machine learning platform. Although most public clouds offer these capabilities, they come as additional add-ons you need to pay for. D2iQ Kaptain is an end-to-end machine learning platform that comes with everything your organization needs to hit the ground running, including hyperparameter tuning and model tracking, security, real-time cost management, and other necessary enterprise-grade functionality that we validate with continuous resiliency, scale, and mixed workload testing, at no additional cost. With an end-to-end ML platform, enterprises can see the gains of their projects and workloads in no time.

Features and Benefits

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D2iQ Kaptain SDK

The Kaptain SDK hides the complexities of Kubernetes and exposes what is relevant to data scientists: training, tuning, and deploying models with Python in notebooks.

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Distributed Training

Dynamically distribute model training jobs to run at scale across a large set of resources, thereby optimizing the cost and performance efficiently.

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AutoML

Optimize model performance using any of five built-in algorithms for hyperparameter tuning: run distributed experiments in parallel to shorten the time to value.

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ML Pipeline Automation and Portability

Automation platform with built-in lifecycle management and operational expertise to achieve greater productivity and reproducibility.

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Notebooks-First Approach

Leverage Jupyter-as-a-Service as the primary interface to reduce friction between data science and machine learning engineering for training, tuning, and model deployment.

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Comprehensive Platform to Support End-to-End ML Projects

Shorten the onboarding of new users and ML initiatives with a complete toolkit powered by Kubeflow and a toolkit of Python deep learning frameworks and NLP libraries.

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Enterprise Security and Multi-tenancy

End-to-end secure enterprise-grade ML platform with multi-tenancy, authentication, and identity services to run entire ML pipelines securely and efficiently.

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GPU Support

Access GPUs (shared resources) in a safe and stable environment, without the hassle of dealing with drivers.

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Resiliency, Scale, and Mixed Workload Testing

Continuous resiliency testing, scale testing, and mixed workload testing to ensure the stability, scalability, and interoperability of key services.

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Member of LF AI and Data Foundation

Enable closer collaboration, integration, and interoperability across AI, ML, Deep Learning, and Data projects.

Resources for Machine Learning and AI on Kubernetes

AI Chihuahua: Why Machine Learning Is Dogged By Failure and Delays

AI Chihuahua: Why Machine Learning Is Dogged By Failure and Delays

Introducing D2iQ Kaptain, The Cloud Native End-to-End Machine Learning Platform

Introducing D2iQ Kaptain, The Cloud Native End-to-End Machine Learning Platform

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How D2iQ Kaptain Works: A Brief Demonstration

Ready To Get Started?