Overview
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
Pricing
- $900.00/month
Vendor refund policy
no refunds, but free to cancel anytime
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Main Installation
- 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
Release notes
Major new features
-
Job schedules Create automated request flows for your Deployments using the new job schedules! Job schedules create cron jobs on the cluster that request instances from your transformer (or model) to prediction or explain. To create a job schedule, go to the job schedules page and click on create schedule
-
Automated drift detection Continuously monitor your production input data distribution and compare to a baseline distribution. View and analyze trends on the Monitoring page of your Deployment and distributions through interactive graphs and set alerts to get notified when significant drift is detected. The monitoring functionality has been expanded to support Jensen-Shannon divergence as a metric.
-
Deployment dashboard Navigate to a Deployment to find the new Deployment dashboard. The Deployment dashboard shows the most important Deployment information in a single overview.
Other changes
- Workspaces can now have multiple owners. Team admins are automatically owners of every Workspace, meaning they have the same rights in every Workspace. Regular users can still be made Workspace owner via the Workspace members page.
- Select which autoscaling type you want to apply to your Deployment to finetune your model reachability. Enterprise and SaaS Scale users can choose between CPU and Concurrency based scaling.
Bug fixes
- Fixed an issue where restoring SageMaker Deployments sometimes failed to update the proper Deployment status
- Fixed an issue where the container logs only displayed a part of all the logs
- Fixed an issue where the Deployments order would change after a while
- Fixed an issue where the unsaved changes weren't properly saved when going back from the Deployment summary
- Fixed an issue where the password visibility toggle in the webhook dialog didn't work
- Fixed an issue where updating a Deployment didn't reuse the environment variables from the previous version of the Deployment
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
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.
Similar products
Customer reviews
Simplified deployment and monitoring of ML models
Useful and responsible tool
Combining MLOps, explainability and AI governance
Provide transparency
Provide trust to end users
Model deployment & updates
Monitoring