Mastering ML Model Deployment: From Manual to Pro-Level Automation

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Deploying machine learning (ML) models is often viewed as a challenging process, especially after investing so much time in defining the problem, training the model, and creating an API. Whether you’re new to deployment or an experienced pro, the key lies in choosing the right strategy for scaling your solutions effectively.

In this post, I’ll share three powerful deployment strategies that will streamline your workflow — from beginner-level techniques to advanced automation. But before we dive in, I’ve compiled all these strategies (and many more) in a free Fullstack ML Handbook. Grab your copy at the end of this post to level up your ML deployment game.

1️⃣ Manual Deployment: A Starting Point for Beginners

This is the most straightforward method. When starting out, you’ll likely push all your Python code and your model (as a pickle file) to a GitHub repository. From there, your DevOps colleague can help by wrapping your model in Docker and deploying it to the infrastructure, such as Kubernetes.

Here’s how it works:

• Push the code and model to a GitHub repo.
• DevOps handles…