After weeks or months of hard work, your data science team has finally built a working AI model. But now what? In order for your model to be truly successful, you need to deploy it into production so that it can start generating results for your business. But deployment can be a tricky process.
It's estimated that 90 percent of all machine learning models never make it into production. That's a staggering statistic, and it begs the question: why? There are a number of reasons why machine learning models never make it out of the development phase and into production, where they can really start to add value for an organization.
In the world of machine learning, there is a lot to think about when it comes time for production deployment. You need packaging and provisioning infrastructure - not just anything will do! Creating an API that suits what we want our model's predictions for can take some work as well so make sure everything matches up properly before going live with this new system...or else suffer through endless Stack Overflow posts due to badly executed integrations.
There are three main considerations when deploying an AI model into production:
1. How to keep the model up-to-date.
2. How to monitor the model's performance
3. How to deploy the model in a way that is scalable and efficient. Let's take a closer look at each of these considerations.
How to keep the model up-to-date
The data that your AI model is trained on will inevitably become stale over time. This is why it's important to have a process in place for retraining and updating your model on a regular basis. There are two primary ways to do this: 1) you can retrain your entire model from scratch on a periodic basis, or 2) you can incrementally update your model as new data becomes available. Which approach you choose will depend on factors such as the size of your data set, the complexity of your model, and the amount of time you have available for training.
How to monitor the model's performance
Once your AI model is deployed in production, you'll need to monitor its performance in order to ensure that it is meeting business objectives. There are many different metrics that you can track, but some of the most important ones include accuracy, precision, recall, and F1 score. accuracy measures how often the model correctly predicts the correct label; precision measures how often the predicted label is correct; recall measures how often the actual label is correctly predicted; and the F1 score combines accuracy and recall into a single metric.
How to deploy the model in a way that is scalable and efficient
When deploying an AI model in production, it's important to consider both scalability and efficiency. One way to improve efficiency is by using feature engineering to reduce the number of features that your model needs to process. Feature engineering is the process of selecting only those features that are most relevant to your task at hand while excluding others that are not essential. This can make your model more efficient by reducing both training time and prediction time.
Using cloud-native infrastructure, you can use fully-managed solutions for model deployment that will accelerate your AI projects. You can get a secure, compliant environment in seconds.
Demo of Model Deployment
In the following demo, I interview the talented Senior Data Scientist Rakshit. He shows how to create an endpoint for a model to later integrate it with an application or business process.
Once the mode is deployed, the job is not over. Data Scientists and Businesses need to continuously monitor and work on better versions of the model That means that at some point, you will need to replace a production model with a new version, and that process can be tricky as well. In the following demo, we show how powerful tooling allows you to replace a production model and how to create multiple copies to support more demand of that model.
AI in production can be a major game-changer for your business
Deploying an AI model into production can be a complex process, but it doesn't have to be daunting. By keeping these three considerations in mind—how to keep the model up-to-date, how to monitor the model's performance, and how to deploy the model in a way that is scalable and efficient—you can deploy the process with confidence and ease.
To increase your chances of success, be sure to consider the volume and quality of data needed to train your model, the compute resources required for deployment, and the skills and expertise needed to get everything up and running smoothly. With these factors in mind, you'll be well on your way to reaping all the benefits that AI has to offer.