Model Deployment in Production
Explore how to deploy machine learning models reliably in real-world production settings. Understand scalability, latency, reproducibility, and monitoring challenges and learn to apply MLOps tools and techniques to maintain model performance and adaptability over time.
We'll cover the following...
A great model in a notebook is just the beginning. Real-world success depends on deploying that model reliably, monitoring it in production, and adapting it over time. In this lesson, we’ll explore the trade-offs and complexities that go into deploying ML models at scale. Let’s get started.
Key considerations in model deployment
Tell me a bit more about the important factors you consider when deploying a machine learning model into production. What are your thoughts on:
Scalability and latency requirements?
Ensuring model reproducibility and consistency across environments?
Monitoring post-deployment to handle issues like model staleness or data mismatch?
What tools and best practices would you use to address these considerations?
Sample answer
Let’s explore each of these aspects with key considerations and relevant tools in the MLOps space.
Scalability and latency ...