Deploying machine learning models into production can be a complex and resource-intensive process. It requires setting up multiple interconnected components to ensure that your inference pipeline operates smoothly and reliably. Among the key building blocks are:
- A Robust HTTP Server: This handles incoming inference requests, ensuring seamless communication between your application and the model.
- Request Buffering and Queuing: Incoming requests may arrive faster than the model can process, especially when handling high traffic or large datasets. Buffering and queuing systems ensure that requests are properly managed without overwhelming the server.
- A Model Execution Framework: This framework ensures your models are executed efficiently, responding accurately to inference requests while maximizing hardware utilization.
- Monitoring Software: It’s important to keep an eye on your inference metrics, server stats and uptimes. Without monitoring software, you would not recognize when things go wrong, or where you can improve your setup.
Each of these components must be carefully set up, monitored, and maintained to ensure the stability and availability of your system. For many organizations, this can mean dedicating significant time and resources to building and managing infrastructure, handling deployment complexities, and maintaining high standards of security.
Our All-in-One Deployment Solution
The refids toolbox simplifies the deployment process by providing an all-in-one solution packaged inside a single, ready-to-deploy container. The deployment container comes pre-configured with everything you need to deploy your machine learning models into production.
| On-premise deployment | Cloud-based deployment |
|---|---|
| Download the container to deploy it to your on servers on-premise. The container includes a production-grade HTTP server, request buffering and queuing mechanisms, and everything else you need for deployment. Just spawn the container and start making inference requests. | Deploy your container with a single click to the cloud. Here, we also manage critical security aspects, such as setting up and maintaining TLS certificates, ensuring that communication between clients and servers is encrypted and secure. |
By using our solution, you reduce the overhead of managing infrastructure and free up your team to focus on what matters most – developing and refining your machine learning models. With our support for monitoring and maintenance, you can rest assured that your inference pipeline remains highly available and secure.
On-premise deployment
Protecting the intellectual property embedded in machine learning models is a top priority for organizations. Many businesses hesitate to deploy their models to external servers or make APIs accessible over the internet due to security concerns. Risks such as unauthorized access, hacking, exploitation, or theft of sensitive company insights can jeopardize an organization’s competitive edge.
The solution to this can be on-premise deployment. By hosting AI models on your internal servers and restricting access to your organization’s intranet, you enhance security. On-premise setups make it far more challenging for malicious actors to compromise or extract your proprietary algorithms and data.
To streamline this process, our toolbox makes on-premise deployment straightforward and efficient. As described above, our ready-to-deploy containers contains everything you need for deployment, tailored to your on-site infrastructure. Just spawn the container and start making inference requests!
Deploy to the cloud
Cloud deployment offers high flexibility. With access to a wide range of hardware configurations, you can customize your deployment setup to match your specific use case. Additionally, cloud resources can be scaled up or down with ease, enabling you to manage fluctuations in request volume without over-provisioning or incurring unnecessary costs.
With our powerful toolbox, you can execute the entire deployment process with just a single click. We handle everything for you, from setting up the server environment, installing necessary software, configuring the request server, to downloading and preparing your models. Our process is streamlined to save you time and effort while ensuring reliability. Furthermore, we put a focus on security. For example, we use TLS certificates to encrypt the communication with the server, keeping it protected and private.
Our solution empowers your business to leverage the benefits of cloud technology without the headaches of managing the technical details. Whether you’re a startup or an established enterprise, our streamlined process ensures a seamless transition to the cloud, allowing you to focus on innovation and growth.
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