If you’re like most people working in the field of machine learning (ML), you know that it can be a powerful tool for solving complex problems. However, managing and deploying ML models can be a real challenge. That’s where ML Flow comes in. It’s an open-source platform that helps you manage the entire ML lifecycle, from experimentation and reproducibility to deployment and a central model registry.
This blog post will give you a simple, hands-on introduction to ML Flow and show you how to deploy an ML model on Azure using Azure Container Instances (ACI) and Azure Kubernetes Service (AKS). By the end of this post, you’ll understand how ML Flow works and how to use it to deploy your models on Azure.
Setting Up ML Flow
To get started with ML Flow, you’ll need to install it first. Don’t worry, it’s easy! Just use pip:
pip install mlflow
After that, you’ll need to set up an ML Flow server to store your experiments and models. You can do this by running the following command:
This will start an ML Flow server on your local machine. You can access the server at http://localhost:5000.
Tracking Experiments with ML Flow
ML Flow lets you track your experiments and compare different runs. To use it, you’ll need to import the mlflow library and use the mlflow.start_run() function to start a new run. Then, you can use the mlflow.log_param() and mlflow.log_metric() functions to log parameters and metrics for the run.
Here’s an example of how you might use ML Flow to track an experiment:
You can view the logged parameters and metrics for your runs on the ML Flow server.
Deploying Models with ML Flow
ML Flow also makes it easy to deploy your models. All you need to do is use the mlflow.pyfunc.serve() function, which starts a web server to serve your model. Here’s an example of how you can deploy a model using ML Flow:
This will start a web server at http://localhost:5000 that serves your model. You can then send requests to the server to make predictions with your model.
Deploying on Azure with ACI and AKS
While deploying models locally with ML Flow is convenient for development and testing, you’ll probably want to deploy your models in a more production-ready environment for others to use. One option is to deploy your models on Azure using ACI or AKS.
Azure Container Instances
To deploy your model on Azure using ACI, you’ll need to create a container image that runs your model. You can do this using Docker.
Once you have your container image, you can use the Azure CLI to create an ACI instance and deploy your container image to it. Here’s an example of how you can do this:
This will create an ACI instance and deploy your container image to it. The container will be available at a public IP address, which you can find using the az container show command.
Azure Kubernetes Service
Another option for deploying your model on Azure is to use AKS. It’s a managed Kubernetes service that helps you deploy and manage containerized applications.
To deploy your model on AKS, you’ll need to create a Kubernetes deployment and service that runs your container image. You can do this using the Kubernetes CLI or by creating a Deployment and Service resource in YAML.
Here’s an example of how you can create a deployment and service for your model using the Kubernetes CLI:
This will create a deployment and service that runs your container image and exposes it on port 5000. You can find the public IP address for the service using the kubectl get service command.
Conclusion
In this blog post, we gave you a simple, hands-on introduction to ML Flow and showed you how to deploy an ML model on Azure using ACI and AKS. ML Flow makes it easy to manage and deploy ML models, and Azure provides a reliable and scalable platform for deploying your models in production.
We hope this helps! If you have any questions or need further clarification, don’t hesitate to reach out to us over LinkedIn!
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