AI Model Deployment is about turning trained models into useful tools that can make predictions, answer questions, or detect issues in real time.
- Testing your model: This is a step when you test your model and fine tune it to ensure that it can handle real world data which can be unpredictable or incomplete or always changing. For example, if you are trying to build a shopping recommendation system, your model learns to stay relevant while customers are clicking or buying stuff.
- Setting up the deployment pipeline: In simple terms, you need to package your model with everything it needs to run such as code, libraries, dependencies, etc. If you want to learn more, read more about Containers or Docker. Once deployed, the model connects to actual systems through APIs. I will write more about Containers and APIs in future.
- Continuous monitoring and maintenance: If you are assuming that your job is done once your model is deployed online, then you may be amazed to know that your model’s accuracy can go down as real-world data changes. This is called ‘model drift’. So, you need to continuously monitor your model as it helps you to spot performance drops early and retrain the model with fresh data, if needed. It is very much akin to regularly maintaining your car.
- Scaling the Model: If you are lucky and your model starts getting popular, you would need to handle more traffic (i.e., more users accessing your model). This is when you need to learn and implement scaling which means preparing your infrastructure to handle bigger workloads.
In nutshell, deployment is a bridge between theory and action. You need to master deployment pipelines, learn how to monitor your solution, and how to scale when you get lucky (after a lot of hard-work).
Like AI, you also need to continuously learn, adapt and create value in the real world. This is one of the steps 😊
𝖲𝗍𝖺𝗒 𝗍𝗎𝗇𝖾𝖽 𝖿𝗈𝗋 𝗍𝗁𝖾 𝗇𝖾𝗑𝗍 𝗌𝗍𝖾𝗉 𝗂𝗇 𝗈𝗎𝗋 𝖡𝖾𝗀𝗂𝗇𝗇𝖾𝗋’𝗌 𝖠𝖨 𝖢𝗈𝗆𝗉𝖺𝗌𝗌 𝗌𝖾𝗋𝗂𝖾𝗌!
#𝖡𝖾𝗀𝗂𝗇𝗇𝖾𝗋𝗌𝖠𝖨𝖢𝗈𝗆𝗉𝖺𝗌𝗌 #𝖠𝖨𝖥𝗈𝗋𝖡𝖾𝗀𝗂𝗇𝗇𝖾𝗋𝗌 #𝖣𝖾𝗆𝗒𝗌𝗍𝗂𝖿𝗒𝗂𝗇𝗀𝖠𝖨
