The development of a machine learning model tends to end before model deployment. Machine learning deployments can be a tedious process. Let check out the best practices that should be adopted for successful model deployment as below.
The development of a machine learning model tends to end before model deployment. Machine learning deployments can be a tedious process. Deployed ML models are being used to solve business problems already.
There are several risks associated with machine learning ML model deployment, however, this makes it important to follow the best practices to deploy machine learning models. The best practices for a successful model deployment are discussed in this blog post.
Some best practices that should be adopted for successful model deployment are discussed below.
1. Availability of large datasets
It is important to use large datasets for the deployment of ML models. Also, the data has to be available in real-time so it has to get fast and accurate predictions. For example; before the machine learning model is deployed, there needs to be a streaming source of the data.
These data can be stored in data warehouses and databases. A data lake environment with easy and efficient access to multiple data sources also needs to be set up. This data needs to be fed to the machine learning models speedily. When there is a well-structured pipeline, it ensures that the machine learning model continuously receives data after it has been deployed.
2. Choosing the right AutoML platform
It is important to choose the right tools for deployment. It is much recommended to use AutoML tools to build the deployment pipeline. It will also help to boost the efficiency of the ML models. AutoML helps to automatically perform feature engineering, data preprocessing, thereby reducing the risk of human error.
It also helps to choose the right ML algorithms. AutoML tools should also integrate with the deployment pipeline. Choosing the right AutoML tools also depends on technical requirements like the machine learning ML model deployment requirements, the type of data, the platform features, etc.
3. Robust deployment approach
It is recommended to have a robust approach to ML model deployment. It can be robust by integrating the model both as an API endpoint and a graphical user interface. And building a smooth ML pipeline architecture so that all the teams can work seamlessly. A complete environment and large datasets also need to be made available.
4. Deployment support and testing
The right ML frameworks need to be chosen for the monitoring, reporting, and logging of results. This will make testing and deployment seamless. The ML deployment pipeline should be tested in real-time and closely monitored, test results can then be sent back to the data scientist to retrain the model if necessary.
For example, the data scientist can add more features to the dataset to improve the machine learning model. Data quality and model performance also need to be closely monitored.
5. Effective communication and management
The successful deployment of machine learning models also depends on good communication between all the teams involved. The Data science teams need to work together with machine learning teams in order to deploy the models.
The machine learning engineers also need to have full control of the systems. Transparent communication is highly recommended during machine learning deployments.
6. Choose an efficient way to serve your ML system
To perform a successful model deployment it is important to choose an architecture that best suits your machine learning system. There are two most commonly used architectures for machine learning ml model deployments, both have their pros and cons.
The pre-computed model prediction architecture is one of the simplest and earliest serving machine learning models. It uses an indirect method for serving models. The predictions are pre-computed for all the possible combinations of input variables and stored in a database.
It is mostly used to develop recommendation engines. The recommendations are stored and shown on the user login. It is cost-efficient and has low inference. Cons are that it is not susceptible to change and does not support continuous variables.
Microservice Based Model Serving: it uses a model that is served independently of the application and predictions are provided in real-time per the user’s request. This architecture provides flexibility for model training and deployment.
The pros are It supports real-time deployment and is highly scalable, Con’s: infrastructure costs, and low latency. It is recommended to choose any of these two architectures for ML model deployment because it also integrates with existing tools.
7. Retraining the Machine learning model after deployment
The machine learning model's performance can reduce overtime after it is deployed. It is very important to retrain this model frequently to maintain a successful deployment. The retraining requirements need to be evaluated. Based on model monitoring, and evaluation, the ML model can be retrained. An out-of-time analysis can be performed to determine the next window for retraining.
A model can be retrained online or offline. Online training is the process of training the model while it is in production. For example, while predicting whether an ad will be clicked or not. It can be hard to implement. Offline training is the process of training the model from scratch. New data is supplied to the model. It is then pushed to production using shadow testing.
8. Simple naming conventions
Naming conventions are highly encouraged for successful model deployment. For example, python recommends naming conventions included in Pep 8: Style guide. Machine learning systems grow as the number of variables grows. When you establish a clear naming convention, it will help machine learning engineers understand the roles of different variables. So that it conforms to the convention as the project expands.
9. Resource utilization
Another important best practice when deploying machine learning models is to utilize resources as much as possible. This model requires system resources, CPU, and GPU. It is important to understand the requirements of the system that is used during the different phases.
This can optimize the cost of the experiment and maximize a budget. Profiling models also offer advantages and help to save costs. Profiling models can help identify and fix bottlenecks like slow training jobs, latency problems.
10. Monitor the predictive service and performance
Another best practice to adopt during model deployment is to monitor the metrics of the machine learning model such as Root mean square error (RMSE), Area under the curve (AUC). These metrics can help evaluate a model's performance with the business objectives.
Machine learning models should be designed to be scalable and be easily deployable. It should be flexible in production. It is highly recommended to adopt any of the best practices discussed in this blog post for successful model deployment.