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.)
  • 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, thoughput, & more.
  • Build your stack in Datatron
    Build your ML stack right from within Datatron.
  • 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.
  • Simplified model containerization now eliminates this manual process from data scientist workflows.
  • 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.
  • Datatron now supports the most common SSO providers that enterprises are already using to streamline secured access to enterprise stacks.

*** WEBINAR ***

“Product Release – Datatron 3.0”

In this informative webinar, you’ll learn about new features including:

  • JupyterHub Integration – Upload, download, share and deploy models right from within your Notebook
  • Kubernetes Management – Create new stacks and deploy Datatron in just a few clicks
  • Enterprise-grade Features – SSO, simplified searchable logs, and much, more…

Streamliney your ML workflows with Datatron’s latest release.

Weds. July 27th, 11 am PT/2 pm ET

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whitepaper

Success Story: Global Bank Monitors 1,000’s of Models On Datatron

A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.

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Success Story: Domino’s 10x Model Deployment Velocity

Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.

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5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

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Life Cycle of Machine Learning Models

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Unique Challenges Of Machine Learning Models In Production

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Model Deployment

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Our Latest *** RELEASE ***

Datatron 3.0

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Datatron continues to innovate and improve data scientist workflows with our latest release, which includes a JupyterHub Integration, Kubernetes Management and more Enterprise-grade features.

See the Release Notes!