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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.
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
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.
Pricing is based on a fixed monthly subscription cost. You pay the same amount each month for unlimited usage of the product. Pricing is prorated, so you're only charged for the number of days you've been subscribed. Subscriptions have no end date and may be canceled any time.
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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.
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
Release notes
Improvements
Updated the SHAP explainer to 0.46. Older SHAP kernel explainer objects are not supported by version 0.46. Use the new explainer framework selector to deploy a different version of kernel SHAP.
Workspace owners and operators can now change the status of alerts
Inverted the trend line color for alerts and errors on the team overview page
The unit of measurement for alerts is now saved and displayed in the triggered alerts overview
Bug fixes
Fixed an issue with upgrading a Deployment to an authenticated external Deployment
Automatically close a popover if an option from the list is selected
Fixed an issue where alerts were triggered when skipLog was used to inference
Disabled the ability to upgrade archived Deployments
Fixed an issue with upgrading a Deployment to an authenticated external Deployment
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.
This product has charges associated with it for deployment support, and the aMiSTACX A51 Monitoring and Control dashboard.
Ubuntu 22.04 LTS developer's stack deployed via an aMiSTACX G6F. Additionally, get AWS dashboard manageability with aMiSTACX's A51 Monitoring & Control Dashboard.
ClimateTracker provides intelligent AI Climate Disclosure reporting tools that guide organisations through changing climate standards, help make impactful climate decisions and reduce compliance costs
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
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