Datatron MLOps Platform vs. Informatica ModelServe

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Datatron MLOps Platform
Score 0.0 out of 10
Enterprise companies (1,001+ employees)
Datatron is an MLOps platform that helps businesses deploy, catalog, manage, monitor, & govern ML models in production (on-prem, in any cloud, or integrated feature-by-feature via our API). Datatron is vendor, library, and framework agnostic and supports models built on any stack, including AWS, Azure, GCP, SAS, H2O, Python, R, Scikit-Learn, and Tensor-Flow. Whether users are just getting started in MLOps, or want to remedy or supplement a homegrown solution, Datatron…N/A
Informatica ModelServe
Score 0.0 out of 10
N/A
Informatica ModelServe is an MLOps solution that helps users to make data science and machine learning simple, open and embeddable.N/A
Pricing
Datatron MLOps PlatformInformatica ModelServe
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Datatron MLOps PlatformInformatica ModelServe
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Best Alternatives
Datatron MLOps PlatformInformatica ModelServe
Small Businesses
Google Cloud AI
Google Cloud AI
Score 8.5 out of 10
Google Cloud AI
Google Cloud AI
Score 8.5 out of 10
Medium-sized Companies
Google Cloud AI
Google Cloud AI
Score 8.5 out of 10
Google Cloud AI
Google Cloud AI
Score 8.5 out of 10
Enterprises
Dataiku
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Score 8.6 out of 10
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Score 8.6 out of 10
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User Testimonials
Datatron MLOps PlatformInformatica ModelServe
ScreenShots

Datatron MLOps Platform Screenshots

Screenshot of "AI Health" Dashboard - See the health of your program in a single pane of glass, including drift, bias, and custom performance metricsScreenshot of Monitor for bias and drift, and data scientists can use Datatron as a starting point to investigate issue root causeScreenshot of Datatron's patented static endpoint allows endless configurations in the gateway, including a/b testing, shadow mode, and canary modeScreenshot of Both real-time inferencing and offline batch jobs can be configured and deployed in the ML gateway.Screenshot of Simplified Kubernetes Management - Provision environments, create clusters, and manage Kubernetes in just a few clicksScreenshot of JupyterHub Integration - Upload, download, register, share, and deploy models right from within your Notebook