Deeploy

Deeploy is a Responsible AI Platform designed to assist businesses in deploying and managing their machine learning (ML) models in a safe and transparent manner. According to the vendor, Deeploy caters to businesses of all sizes, from small startups to large enterprises, and is utilized by data scientists, machine learning engineers, AI researchers, data analysts, and IT professionals across various industries such as banking, fintech, healthtech, manufacturing, and insurance.

Key Features

Manage, Deploy, and Update: According to the vendor, Deeploy allows for easy deployment and updating of AI models through simple steps. Users can collaborate using Deeploy's user interface, API, and Python SDK, and integrate Deeploy into their applications using API endpoints.

Explainable AI: The vendor claims that Deeploy supports deployments with local and global explainers, allowing users to add custom explainers tailored to their end-users. Deeploy also offers the ability to traceback explanations of historical predictions.

Feedback Loop: Deeploy aims to improve model performance by facilitating interactions between AI and humans. According to the vendor, users can provide real-time feedback on models and predictions, enabling domain experts to evaluate predictions and enhance the overall performance.

Integrations: Deeploy can seamlessly integrate into existing workflows, according to the vendor. Users can run Deeploy on their current cloud platforms such as AWS, GCP, and Microsoft Azure, and incorporate Deeploy into existing code workflows with platforms like GitLab and CircleCI. Notifications can be received through channels such as Slack and email.

Audit Trails: According to the vendor, Deeploy offers the capability to reproduce and traceback decisions and explanations. It supports processes to comply with AI regulation and ensures clear ownership of Deployments and Workspaces by tracing and reproducing every change, prediction, explanation, and deployment.

Monitoring and Alerting: Deeploy provides constant monitoring of model drift, bias detection, and performance, according to the vendor. Users can define custom alerts for the metrics that matter to them and easily identify the root cause of model degradation.