Machine learning model deployment is the process of implementing the developed model in a live environment. Machine learning deployment incorporates the principles of MLOps to ensure that the models are deployed and maintained reliably and efficiently.
Deployment is one of the key stages in the software development cycle. But deployment from a local test environment to a real-world application can get complicated. Improper deployment can lead to wastage of time, money, and resources.
Hence, businesses must ensure that the deployment is done correctly. To help you with the machine learning model deployment process, in this article, we have discussed some of the best ways to do it. But first, we will take a look at some of the common challenges faced in machine learning deployment.
Challenges With Machine Learning Deployment
The main challenges with machine learning deployment include:
- A communication gap between the development team and the deployment team. Skills and expertise don’t usually overlap in these distinct areas. This leads to inefficiencies in the deployment process.
- Non-availability of the right infrastructure and environment for deployment.
- Difficulty in monitoring model accuracy in a real-world environment.
- Scalability.
- Explaining predictions and results to stakeholders.
These challenges can be overcome by following the best MLOps deployment practices. But before we move to it, let’s take a look at what deploying machine learning models actually is.
Deploying Machine Learning Models
As mentioned earlier, machine learning model deployment is a tricky task. It will differ generally based on two main components;
- The type of machine learning model
- The system environment of the model
The general deployment process consists of four steps. They are:
- Developing and creating a model in a training environment
- Testing and cleaning the code
- Preparing for container deployment
- Planning for continuous monitoring and maintenance after deployment
Here is a look at the best ways to deploy machine learning models.
Best Ways to Deploy Machine Learning Models
There is no one-size-fits-all approach. Machine learning deployment depends upon the industry, business, and the type of application. But, there are some common practices that businesses can use across industries. The following are the four best ways to deploy machine learning models to ensure reliability and efficiency.
Testing the Code
All modern software projects include unit code testing. And machine learning projects are no exception. Testing the code helps determine if the code is of sufficient quality to be deployed.
In most cases, the model is developed in an offline environment. Thus, testing, scrutinizing, and streamlining the code is essential. It ensures the machine learning model functions in a live environment as planned.
Additionally, a detailed documentation file should be prepared. It makes sure that every stakeholder has clarity of the working of the machine learning model. It should provide a detailed description of the testing process and the outcome achieved.
Containerization
Containerization is a powerful tool in machine learning deployment. The containers have all the elements needed for the machine learning code to function. Assuming that your business has the expertise in working with them, they are the best-suited to a majority of machine learning applications.
The benefits of using containerization for machine learning deployments include:
- Scalability
- Straightforward updating and deploying distinct areas
- Lowers the risk of downtime
- Offer a good developer experience
You can use various platforms for managing containerized workloads and services. One such popular platform is Kubernetes. Kubernetes helps with automating container management. You can use it to automate the monitoring, scheduling, and scaling of the machine learning models.
Using a Model Registry
Generally, every organization has a common, enterprise-wide model registry for all machine learning operations. It acts as a location to store machine learning models as they are trained. This helps simplify the bookkeeping process during R&D.
Having a model registry helps cut the communication gap between data scientists and the engineering team. This helps create better, more efficient models in a short period. Similarly, if the machine learning model gives a wrong output, registries come in handy. They can be used to determine which model is causing the issue.
There are various types of model registries. Businesses can choose one according to their needs. Some of the options available are:
- Cloud-based registries
These are best suited for businesses having a single cloud service provider.
- Open-source registries
Best-suited for small and medium businesses, who can’t afford to spend on a provider.
- High-end registries
Incorporated into AutoML tools, and useful for large organizations. They also cost more than the other options mentioned on this list.
Using Feature Stores
A feature store is a repository. It helps data scientists keep track of features they have developed for the machine learning models. The developed features can also be used to train new models by everyone who has the necessary skill set. Feature stores work best with organizations that use data entities that are applicable to different models.
The biggest benefit of using feature stores is that using them accelerates the machine learning building and deployment process. Data scientists can iterate new versions more quickly by reusing past work.
Beyond Model Deployment
Successful machine learning deployment goes beyond the initial deployment. Continuous monitoring is needed to make sure that the application is working as intended. It also ensures that the software is optimized and data drifts or outliers are avoided.
Monitoring models after deployment can be a challenge. But, it is vital to ensure the success of the software. Businesses can either do it manually or automate the process, depending upon the scale, the application, and budget requirements.
Parting Thoughts
Deploying machine learning models can be challenging. It is a problem faced by most businesses. But, by developing the right MLOps expertise and using the best deployment ways, the challenges can be mitigated. Your organization will see many tangible and intangible benefits and experience rapid growth with the right deployment methods.
We hope that the methods mentioned above can help you with the machine learning model deployment process. So, what methods will you employ at your organization to deploy machine learning models? Do let us know.
