How to Deploy ML Models in Production (Flawlessly)
4 Things to Keep in Mind Before Deploying Your ML Models
As a Cloud Engineer, I’ve recently collaborated with a number of project teams, and my primary contribution to these teams has been to do the DevOps duties required on the GCP Cloud.
To learn more about me, read the following:
Regardless of the project, it might be software development or ML Model building. My main goal as a DevOps Cloud Engineer is to achieve four objectives. What are they?
Reliability
Scalability
Security and
Maintainability
In this article, I’ll highlight four things you should bear in mind while deploying your ML models in production because the framework I’m providing will help you achieve all four of the goals I described before.
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1. Use a Version Control System for Your Models
First and foremost, how can we ensure the reliability of machine learning models? I’ll say, employ version control systems.
But how does it do? To better understand this, let’s define version control systems. Version control is used to keep track of different versions of your software or models.
So, if we can track and control these versions when a version fails after production, we can still utilize the most stable version to ensure the reliability of our software or ML models. I’m making sense till now, right?
Personally, I recommend that you use Git, which is one of the most common version control systems used in practically all companies.
If you want to learn Git from me, leave a comment, and based on the responses, I will create a thorough Git Guide centred on “How we use it in any projects”.
Here are some hands-on activities that we require as the bare minimum in every project.
Learn these commands! They help us monitor changes, manage branches, and collaborate with our team members.
Set Up Git:
Create a directory for your project:
mkdir SampleProject
cd SampleProjectInitialize a Git repository:
git initAdd files to Git and commit changes:
git add <file_name>
git commit -m "Initial commit"Branching Strategy:
Use branches to experiment with new features or bug fixes:
git branch new_branch
git checkout new_branchMerge branches after completing changes:
git checkout master
git merge new_branchMaintain Model Versions:
Use tagging to label versions:
git tag -a v1.0 -m "First model version"2. Perform Canary Deployment
Scalability follows on from reliability. So, how do we scale our ML models? Scaling up is not always synonymous with growth.
Consider this: you have a model that is in the most stable state and has the best features. Now you realize it’s time to scale up, so you tweak some features you felt were more advanced and would be appreciated by people, but the results are bad. And the drop begins.
This is not what you desire, is it?
“Canary Deployment” is used in this situation. Here, a small percentage of all users will be exposed to the new model modifications, and error rates, latency, and user comments will be monitored. so that you can revert to the earlier model version in case of any problems.
So, what are we going to do? Now, let’s examine this step-by-step:
Set Up Load Balancing:
Deploy the new model to a limited set of servers.
Configure a load balancer to route a small percentage of traffic to the new model.
Monitor Key Metrics:
Track error rates, latency, and user feedback.
If issues arise, roll back to the previous model version.
3. Secure Your Models in Production
What about security now that we know how to make our model scalable and reliable in production? Based on my experience, I will say that production models need to be secured against a variety of threats.
You must employ safeguarding techniques like authentication, authorization, encryption, and others to tackle these threats, which can range from access attacks to compromises of a model’s integrity (sensitive data).
Here’s how I typically do it:
Access Controls:
Use authentication and authorization mechanisms to control access.
Example: Implement API keys or OAuth for accessing endpoints.
Encrypt Sensitive Data:
Protect model parameters and input data using encryption.
Regular Audits:
Review training and test data periodically.
Perform random tests on model predictions to detect anomalies.
4. Monitor the Performance of Your Model in Production
The last objective is maintainability. As a data expert, you should also learn how to keep your models up to date while they are in use.
What does model maintenance mean?
To ensure that nothing goes wrong, you must constantly check the performance of your model that has been put into production.
Additionally, you must keep an eye on how much CPU, memory, disk, and network input and output are being used.
Why, you ask? — This indicates how effectively your model is operating.As a data scientist, your primary responsibility is also to keep an eye on the
drift.
Adrift, or variation in the model’s input data, shows if the data you feed the model is the same as the data you used to train it. Because your model is out of date if it isn’t. You need a new one.
Thus, these are a few methods for keeping your model in production.
How to proceed step-by-step:
Set Up Resource Monitoring:
Track CPU, memory, and disk usage to identify bottlenecks.
Monitor Data Drift:
Check if input data distribution changes significantly from the training data.
Tools like Evidently or WhyLabs can automate drift detection.
Meet SLAs:
Regularly evaluate performance against Service Level Agreements (SLAs).
Well, these are the 4 things/practices you need to keep in mind before deploying any ML models in production.
In addition, if you ask me, what are the best tools for deployment? I suggest doing this.
Tools
1. Docker:
Package your model and dependencies into a Docker container:
docker build -t model_name .
docker run -p 5000:5000 model_name2. Kubernetes:
Use Kubernetes for orchestrating multiple containerized models.
3. Cloud Platforms:
Services like AWS SageMaker, Google AI Platform, or Azure ML simplify the deployment process.
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