Emily is the developer toolkit for helping engineers and data scientists in developing and deploying microservices.
Emily allows you to build and deploy production-ready AI microservice systems while you can focus on the important part – the AI. Everything from project creation to final deployment in a production environment will be orchestrated by Emily
Build ML microservices with ease.
Emily Core comes pre-packaged with everything you need to start building intelligent AI systems.
Start immediately building projects with production-ready templates for ML microservices, including REST and gRPC APIs with pre-configured machine learning images for Natural Language Processing, Computer Vision, and generic machine learning workloads.
Your end-to-end solution for rapidML microservice deployments.
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Once built, Emily Deploy can help you easily deploy your microservice to virtually any destination.
Emily Deploy contains user-friendly flows for configuring servers and deployment environments, enabling developers to deploy a microservice to production in seconds.
Emily is the developer toolkit for helping machine learning engineers and data scientists in developing and deploying machine learning microservices. Put simply, Emily is a CLI tool that orchestrates everything from project initialization to final deployment in a production environment.
Creating an Emily microservice starts with selecting one of several machine learning templates, all containing production-ready REST or gRPC APIs preconfigured to best-practice standards in terms of scalability, performance, and resilience, and transparency.
Emily mounts the developer’s code editor onto a Docker container to ensure a fully containerized development setup. In fact, the developer will write code, test, and run their service from inside the very Docker container that will later go into production.
Because the development environment is fully containerized, developer-setup conflicts, differences in OS and driver versions are a thing of the past; If the service runs locally, it will run in production.
Getting to production can normally be a hassle, but Emily resolves this hassle. The Emily CLI provides simple-to-use commands for deploying Emily microservices to arbitrary deployment targets in a safe, consistent, and reproducible manner.
Emily provides automated deployments to any SSH-accessible self-hosted VM solution, as well as Kubernetes clusters (with explicit support for Azure AKS and Azure Container Registries if so desired).
No, you keep your data on a server controlled by you.
Yes, you are completely in control of the files used in your Emily projects and can continue to access these even after removing Emily.
Subscription automatically renews, but you may cancel your Emily subscription directly in your Emily account.
Great news for Nginx users – you can now secure your APIs quick and easy.
With emily@3.0.2, APIs can be secured with an HTTPS reverse proxy using the new Nginx configuration command.
In the Nginx proxy, all requests are routed through HTTPS, and SSL certificates are automatically renewed.
Control your data by accessing and keeping your critical data locally
Work together in a controlled environment when developing your AI solution
Easy deployment with AI enabled docker containers
“As our work involves automating data and decision processes for our customers, we’ve taken a dose of our own medicine and created an AI platform that can automate parts of the deployment process. The time we save by using the platform benefits our customers. We can pour our energy into the tailormade solution part of the development and spend less time on basic configuration and deployment. In short, we’ve automated the “boring” part of the development process – the part that can take an unnecessarily long time or cause frustration.
We use the platform ourselves and are now making it available to all other developers.”
Practical approach to AI – no hot air or unnecessary projects.
Clear agreements and flexible forms of cooperation to the extent necessary.
An experience of value right from day one, and usually a very short ROI.
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