Simplified AI/ML Operations
Why Kaptain for AI/ML Kubernetes?
Kubernetes is a natural fit for artificial intelligence (AI) and machine learning (ML) because of its ability to meet the scalability needs of AI/ML workloads as well as the continuous development nature of AI/ML models.
There are also organizational challenges in assembling the teams required to move AI/ML models from development to production.
Increased Productivity at Lower Cost
Kaptain AI/ML breaks down operational barriers to streamline and accelerate the process of getting models into production and improve the success of AI/ML projects, while reducing the total cost of ownership (TCO) of running AI and ML at scale.
End-to-end AI/ML requires a curated and tested set of components that extends beyond notebooks to include model frameworks, libraries, tools, observability, and security required for AI/ML model development and deployment. Kaptain AI/ML meets these needs by bundling best-of-breed open-source technologies to build, automate, deploy, track, and monitor models across their entire lifecycle.
Fully automated deployment with integrated observability, rich security, and cost management make Kaptain AI/ML enterprise-ready for day-2 operations out of the box.
Reduced Complexity, Increased Business Results
AI/ML are incredibly complex, resource-intensive processes with high failure rates.
At the core of Kaptain AI/ML is Kubeflow, an open-source framework for developing, managing, deploying, and running scalable and portable machine learning workloads on Kubernetes. While Kubeflow and Kubernetes would seem to be an ideal way to address AI/ML challenges, the steep learning curve of Kubeflow and Kubernetes can introduce complexities for data scientists and data engineers, many of whom do not have the technical know-how to design or manage these technologies.
Kaptain AI/ML reduces the complexity of Kubeflow and Kubernetes through automation, tooling, and integration of a select group of services. Kaptain AI/ML supplements Kubeflow with a carefully curated set of Kubeflow projects (20+ Kubernetes operators), an SDK that simplifies and abstracts Kubeflow functionality, and additional components (like Spark and Horovod) that are not part of the base Kubeflow ecosystem.
Likewise, Kaptain AI/ML provides the enterprise-level security that organizations require in production environments, which also is absent in the open-source Kubeflow version. These enhancements make Kaptain a significantly more capable and consumable enterprise-grade distribution of Kubeflow.
Kaptain AI/ML is the only Kubernetes-based AI/ML platform that has made Kubeflow enterprise-ready through these engineering modifications and extensions. Through these Kubeflow enhancements, Kaptain AI/ML enables customers to go from prototype to production in a matter of days or hours, rather than weeks or months, and to manage entire AI/ML pipelines on any infrastructure.
Fully Loaded to Harness AI Power with Greater Ease
Kaptain AI/ML is an add-on to D2iQ’s DKP Essential and DKP Enterprise
Kaptain AI/ML comes with everything an organization needs to hit the ground running, such as hyperparameter tuning, model tracking, military-grade security, real-time cost management, and other necessary enterprise-grade functionality at no additional cost.
Built-in and fully tested support of GPUs enables analysts to focus on conducting deep learning operations without having to manually deploy drivers. In-depth notebook tutorials help speed up onboarding, time to value, and production readiness of AI/ML models at scale.
Complete Deployment Flexibility and Agility
DKP, from which Kaptain AI/ML is deployed and managed, is built on pure upstream open-source components
Kaptain AI/ML can be deployed on any cloud, multi-cloud, hybrid-cloud, on-premise, and air-gapped environment, wherever required for mission-critical AI/ML initiatives. This gives you the agility to deploy and manage AI/ML operations in public clouds, private clouds, and secure on-premise locations, and to easily change and migrate deployments without incurring excess cost.
Kaptain AI/ML Features and Benefits
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.
Dynamically distribute model training jobs to run at scale across a large set of resources, thereby optimizing the cost and performance efficiently.
Optimize model performance using any of five built-in algorithms for hyperparameter tuning: run distributed experiments in parallel to shorten the time to value.
AI/ML Pipeline Automation and Portability
Automation platform with built-in lifecycle management and operational expertise to achieve greater productivity and reproducibility.
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.
Comprehensive Platform to Support End-to-End AI/ML Projects
Shorten the onboarding of new users and AI/ML initiatives with a complete toolkit powered by Kubeflow and a toolkit of Python deep learning frameworks and NLP libraries.
Enterprise Security and Multi-tenancy
End-to-end secure enterprise-grade AI/ML platform with multi-tenancy, authentication, and identity services to run entire AI/ML pipelines securely and efficiently.
Access GPUs (shared resources) in a safe and stable environment, without the hassle of dealing with drivers.
AI/ML in a Box with NVIDIA
Get a robust Kubernetes platform, a powerful AI/ML platform, and the NVIDIA DGX in a certified and tested solution.
Member of LF AI and Data Foundation
Enable closer collaboration, integration, and interoperability across AI/ML, deep learning, and data projects.