Overview

1/1

Nowadays, transparency, explainability and security of AI models is more important than ever. Having a safe and secure environment to deploy your models enables you to continuously monitor your model performance with confidence and responsibility.

Easily integrate Deeploy Core with your existing AWS stack. Deploying and maintaining ML systems requires involvement of people and tools. Deeploy Responsible AI software giving data science teams autonomy to create and maintain their models.

The challenges Deeploy solves:

A safe and responsible MLOps environment: organized and monitored deployments
Explain and understand AI decisions: create human-AI interaction with experts
Traceback how decisions are made: be able to correct, report and reproduce.

Highlights

A safe and responsible MLOps environment: organized and monitored deployments
Explain and understand AI decisions: create human-AI interaction with experts
Traceback how decisions are made: be able to correct, report and reproduce

Details

Sold by

Deeploy

Features and programs

Financing for AWS Marketplace purchases

AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.

View financing details

Pricing

Free trial

Try for free

Try this product at no cost for 14 days according to the free trial terms set by the vendor.

Deeploy Core - Usage

Info

View purchase options

Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.

Usage costs (1)

Info

Dimension	Description	Cost/unit/hour
Hours	Container Hours	$0.07

Vendor refund policy

Deeploy Core is not eligible for refunds, but customers are free to cancel anytime.

Legal

Vendor terms and conditions

Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

Content disclaimer

Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

Usage information

Info

Version

Delivery details

Main installation

Supported services: Learn more

Amazon EKS

Container image

Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.

Version release notes

IMPORTANT: this release updates KServe to v0.13.1. DO NOT update kserve CRD definitions prior to upgrading Deeploy to v1.42.0. It is safe to upgrade after to v0.13.1

New features

Assign a risk classification to your Deployment
Assign a risk classification that denotes the risk of your machine learning application according to the EU AI Act risk classification.
Added support for batch explanations
Request batch explanations just like batch predictions, generating individual prediction logs for easy tracking.

Improvements

Updated Kserve version to v0.13.1
Improved metadata parsing error handling
Improved error handling and usability of blob credentials
Improved performance (reduced latency) of inference endpoints
Added an installation status page for Enterprise users

Bug fixes

Fixed issue of visible deleted deployments in team overview
Fixed stuck deployment on invalid repository, branch name or commit
Fixed the formatting of errors in the alert rule dialog
Fixed an issue where auto scaling seemed to be removed in the Deployment summary
Fixed an issue where a team admin appeared to have no workspaces
Archiving Azure Machine Learning Deployments now behaves correctly

Additional details

Usage instructions

The general installation steps are as follows: a. Make sure to follow the installation steps as described here: https://docs.deeploy.ml/category/amazon-eks (start at step 2, since you already subscribed to the marketplace listing) b. Install the Deeploy software requirements and helm chart. For the latest stable release checkout: https://artifacthub.io/packages/helm/deeploy-core/deeploy . Use the Deeploy helm chart repository and follow the instructions in the README: https://gitlab.com/deeploy-ml/deeploy-install .

Resources

Vendor resources

Website

Documentation

Support

Vendor support

Default community support is included. Additional support and SLA are available on request: sales@deeploy.ml .

AWS infrastructure support

AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

Get support

Similar products

Deeploy Core - Monthly

By Deeploy

Deeploy Core includes the core functionality of our Responsible AI Platform that enables companies to stay in control of their ML models. Easily bring truly responsible models into production, without compromising on your governance requirements, even for high-risk use cases.

View product

Customer reviews

Write a review

Ratings and reviews

Info

0 ratings

5 star

4 star

3 star

2 star

1 star

0 AWS reviews

3 external reviews

External reviews are sourced from G2 and are not included in the star rating for this product.

Information Technology and Services

Simplified deployment and monitoring of ML models

Reviewed on Nov 24, 2023

Review provided by G2

What do you like best about the product?

Ease of use and intuitive flow allows team members with limited engineering knowledge to deploy and then monitor ML models in production.

What do you dislike about the product?

Error messaging has been quite uninformative in some cases, but I have to admit that this has improved in recent updates.

What problems is the product solving and how is that benefiting you?

Deeploy speeds up deployment and monitoring of models, especially in a data team with limited capacity (FTE) and engineering knowledge.

Useful and responsible tool

Reviewed on Nov 16, 2023

Review provided by G2

What do you like best about the product?

Deeploy has increased productivity as my data science team can now deploy all models in the same place, which allows us to better manage models and monitor their performance. But what really sets it apart is the possibilities it brings for explainability and the use of human feedback.

What do you dislike about the product?

The documentation was still a bit limited

What problems is the product solving and how is that benefiting you?

Ease od deploying and explainability feature that was out-of-the-box

Combining MLOps, explainability and AI governance

Reviewed on Sep 18, 2023

Review provided by G2

What do you like best about the product?

The ease to deploy an AI model, with out-of-the-box explainability and the necessary governance & compliance tools and monitoring functionalities. It also runs smoothly on both Azure and AWS, when working with our customers.

What do you dislike about the product?

We're waiting on a few more integrations.

What problems is the product solving and how is that benefiting you?

Stay in control (governance / compliance)
Provide transparency
Provide trust to end users
Model deployment & updates
Monitoring