Definition
Model deployment is the step within the machine learning life cycle where a new model moves into a production environment and becomes accessible to end-users.
Overview
Designing, developing, and training a machine learning model is a great first step, but if you actually want people to use your model, you need to deploy it. This is where model deployment comes in. As the process that makes machine learning models available in production environments, model deployment is a key step in the broader machine learning life cycle.
Keep reading for details on model deployment, including how it works, which challenges may arise in the context of deploying machine learning models, and best practices for model deployment, including in the realm of security.
What is model deployment?
Model deployment is the process of moving a machine learning model into a production environment where end-users can interact with it. Once deployed, a model performs what’s known as inference, which means it makes predictions or generates content based on new input.
Prior to deployment, models undergo other processes that take place earlier in the machine learning life cycle. These include model design (which is when developers and data scientists determine how a model should operate), development (the process of writing the code that powers a model’s algorithms), and training (which allows a model to recognize relevant patterns by assessing large volumes of data).
The model deployment process is akin to application deployment in the context of the software development life cycle (SDLC). Just as software developers must move a new written application into a production environment before users can access it, machine learning developers must deploy a model into production to make it available for real-world use.
Model deployment vs. model serving
Model deployment is similar to model serving, but these are distinct processes.
Whereas model deployment is the process of moving a trained model into a production environment, model serving is the process of providing a means of allowing users to interact with the model, typically using APIs or a Web service.
Technically, you could deploy a model without serving it – this would mean that you moved the model into a production environment but haven’t given end-users a way of accessing the environment or model. But if you want your model to be usable, you need to serve it in addition to deploying it.
One could argue that model serving is a stage in the broader process of model deployment, but even so, it’s important not to conflate deployment with serving because they refer to different activities.
How does machine learning model deployment work?
The exact steps in model deployment can vary depending on factors like how a model is being used and which type of environment is necessary to host it. In general, however, the machine learning model deployment process boils down to the following key activities:
- Choosing a production environment: Engineers select an environment that is appropriate for hosting the model in production. To make the right choice, they should consider factors such as whether the model requires specialized hardware, how scalable the host infrastructure needs to be to accommodate fluctuations in model usage, and which levels of model latency are acceptable.
- Migrating model data layer: Machine learning models typically require data resources to be present in production environments to support inference. If the data layer is decoupled from the model itself, the data can be deployed separately from the model.
- Deploying the model: In addition to copying the model into production, the team can migrate the model itself.
- Serving the model: As noted above, it’s necessary to serve the model by exposing it through an API or Web service so that users can interact with it.
To streamline the migration process and reduce the risk of errors due to issues like incomplete data transfers, teams may package models using technology like containers. Containers are beneficial in the context of model deployment (as well as software deployment more generally) because they provide a consistent way of packaging and running a model.
This means that differences between the environment where the model training and testing environment and the production environment are less likely to cause unexpected behavior. If the model ran as a container during both testing and in production, it should operate consistently in both types of environments.
Challenges in deploying machine learning models
A variety of factors can complicate successful model deployment. Common challenges – along with potential solutions – include:
- Infrastructure cost: Models can consume substantial CPU and memory resources, leading to high costs post-deployment. For this reason, teams should be careful to select cost-effective hosting infrastructure.
- Inconsistent environments: As mentioned above, differences between testing and production environments could lead to unexpected model behavior after deployment. Packaging models in a consistent way using technology like containers helps to mitigate this issue.
- Security and compliance: Models may contain sensitive data, making it critical to manage resources securely when deploying a model into production.
Potential for model misuse: Once deployed and available to end-users, models could potentially be abused through techniques like prompt injection. To mitigate these risks, teams should deploy and serve models in ways that help to prevent misuse; for example, APIs could be designed to block users who submit a very large number of queries in a short period of time, since this activity may be an automated effort to inject malicious prompts and evaluate how the model responds.
Best practices for model deployment
To streamline model deployment and set your model up for success during inference, consider the following best practices.
Maintain environment consistency
Once again, maintaining consistency between testing and production environments is a key step in avoiding unexpected model operations.
If the environment in which model inference takes place does not closely resemble the testing environment, there is a risk that your tests did not catch model performance or security issues that only occur under specific conditions that are present in production but not testing.
Use version control
To make it easier to recover from errors during or after deployment, use version control tools. In addition to managing the code within the model itself, version control can also manage configuration code that governs how the model behaves.
When you automatically track changes to versions over time, you can quickly and easily roll back to an earlier version of a model in the event of a performance or security issue.
Manage security risks
Security risks can arise at any stage of the machine learning life cycle, not just deployment. That said, it’s critical during deployment to mitigate potential security risks such as unauthorized access to models or associated data while a model is in the process of being migrated into a production environment.
Likewise, teams should be careful to ensure that all layers of the production environment host infrastructure – physical servers, virtual machines, operating systems, container runtimes, and so on – are secure and up to date so that attackers cannot abuse them as a way of breaching the model host environment.
Automate Model Deployment
Developers can automate model deployment by automatically moving a model and its data layer into production after testing is complete.
Automated deployment is beneficial not just because it saves time, but also because it reduces the risk of problems due to human error. It also enables a consistent, predictable deployment process that you can easily repeat every time you need to deploy a new model or a model update.
Choose the right infrastructure
In most cases, model inference can take place using any type of infrastructure – whether physical or virtual servers, or an on-prem or cloud-based environment. That said, depending on model use cases and performance goals, one type of infrastructure may be better than another.
For instance, if the number of users who will interact with your model is likely to fluctuate frequently, deploying the model to cloud infrastructure may be advantageous because you can quickly scale the infrastructure up and down. Likewise, deploying models at the network “edge” rather than in central data centers can be helpful in cases where ultra-low latency is a priority because edge model deployment minimizes the time it takes for data to flow between users and the model.
Streamlining model deployment with JFrog
By streamlining the management of models at all stages of the machine learning life cycle, JFrog makes it easy to move models from development, to testing, and into deployment – while also ensuring smooth integration with popular ML frameworks through JFrog’s APIs and SDKs. What’s more, JFrog’s security capabilities keep your ML code and artifacts secure across all stages of the development process.
