Lecture 35: From Code to Cloud: Mastering System Deployment and Monitoring
This lecture dives into the essential steps of transforming your system from a local project into a fully deployed and monitored solution. We'll cover:
Serialization with Pickle: Learn how to save and load your models for deployment.
REST API Development: Build robust APIs with Flask and FastAPI to serve your system's functionality.
Cloud Deployment: Explore strategies for deploying your model to industry-leading platforms like AWS, GCP, Azure, or Heroku.
System Monitoring: Implement essential monitoring techniques to track your system's health and performance in production.
Join us for an in-depth lecture designed for aspiring and seasoned developers and data scientists looking to master the lifecycle of AI model deployment and system maintenance. This session, "From Model to Market: Navigating Deployment and Maintenance of AI Systems," covers a wide range of critical topics, from serialization with the Python Pickle module to deploying your model across major cloud platforms such as AWS, GCP, Azure, and Heroku.
We’ll start with an introduction to the Pickle module to understand how to serialize Python objects for a smooth transition between modeling and deployment phases. Next, we’ll explore the essentials of creating REST APIs and Web services using popular frameworks like Flask and FastAPI, discussing their specific advantages for different deployment scenarios.
The lecture will then guide you through the steps to deploy your model to the cloud, comparing key features, costs, and scalability options of top providers to help you make the best choice for your application. Lastly, we’ll delve into monitoring strategies to ensure your system performs reliably and efficiently post-deployment.
This comprehensive lecture is a must-attend for anyone looking to enhance their skills in deploying and maintaining robust AI systems. Whether you are deploying your first model or scaling existing systems, these insights will prepare you to handle real-world challenges effectively.
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