Datatron 3.0
New Features Release
JupyterHub, Kubernetes ML Management, and More
Enterprise-Grade Features
Datatron 3.0 Features
Datatron continues to innovate in the AI/ML MLOps space to deliver best-in-class features to help businesses generate more value from machine learning models. Datatron 3.0 release features give data scientists even more tools to streamline workflows that get models into production quicker, more efficiently, and to remedy issues, unleashing the true value of AI.
JupyterHub Integration
Datatron continues to lead the way with simplifying data scientist workflows and delivering value from AI/ML.
- Simple model upload/download & registration – Never leave the Notebook environment you are already familiar with (no context switching)
- Download & share models – List, download, and share models so other data scientists can make edits for other use cases supporting rapid iteration and validation as part of a robust ML lifecycle.
- Autocontainerize models – Eliminate the most inconvenient aspect for data scientists to get their models into production (avoid templates, container wrappers, text editors, etc.)
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![JupyterHub integration with Datatron]()
JupyterHub integration supports commands to upload/download models right from within the Notebook you are familiar with. -
![JupyterHub - Query to see Model list]()
Query to see a list of all models in the Datatron “Model Catalog.” -
![JuypterHub Autocontainerize & Register Model]()
Containerize and register models with just a few arguments. -
![Access JupyterHub from the Datatron dashboard]()
One-click access to JupyterHub directly from the Datatron dashboard.
Simplified Kubernetes Management
Eliminate the complexities of Kubernetes management and deploy new virtual private cloud environments in just a few clicks
- Simple Stack Building – Build and deploy VPC, OS, K8, Network, Storage, & Monitoring on the most popular stacks (AWS, Azure, GCP).
- Operational Dashboard – View real-time performance metrics, such as usage, throughput, & more.
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![Build your stack in Datatron]()
Build your ML stack right from within Datatron. -
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Connect your cloud accounts and add a new cluster in seconds. -
![Specify cluster type from VPC providers]()
Specify the cluster type from the major cloud providers. -
![Specify instance type]()
Granular control of instance type for VPC providers. -
![Edit your cluster scripts directly in Datatron]()
Customize your cluster configuration with direct access to scripts. -
![Select cluster type]()
Select the ideal managed Kubernetes type, including AKS, EKS, TKE, and GKE (coming soon). -
![Cluster selection menu]()
Cluster menu supports multiple configuration types to satisfy your particular needs. -
![Step-by-Step instructions]()
Clear step-by-step instructions ensure your cluster will be up and running in minutes. -
![Kubernetes dashboard]()
Kubernetes dashboard provides an overview of environment performance metrics in a single pane of glass view to understand how your clusters are performing. -
![Charts show performance metrics across pods, deployments, jobs, and more.]()
Real-time charts show performance metrics across pods, deployments, jobs, and more so you always know the health of your systems. -
![Search and filter events as they occur in real-time.]()
Search and filter Kubernetes events as they occur in real-time.
Enterprise Feature Enhancements
Streamlined features that improve operational workflows, enforce enterprise-grade security, and simplify troubleshooting.
- Autcontainerization – Simplified model containerization eliminates the manual process from data scientist workflows.
- Single-Sign-On – Streamlined login to enterprise stacks.
- Simplified Event Logging – Consolidate logs into a single view for the operational team. Simplifies searches and aids in troubleshooting.
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Consolidated logs allow searching of all of your log files in one convenient location to troubleshoot issue root cause in less time. -
![See visualizations of usage.]()
See visualizations of usage.
