Pipeline Deployment to Production

1. Pipeline Deployment to Production

In the previous chapters, we explored the pre-deployment stages of a data and ML pipeline, including

2. Pre-deployment steps

Acquiring and preprocessing the data from an API,

3. Pre-deployment steps

Running experiments to test and tune multiple forecasting models,

4. Pre-deployment steps

and setting up an automated pipeline with Airflow to refresh data and forecasts.

5. Pre-deployment steps

Now, we'll shift our focus to the final piece of the puzzle - deploying the pipeline to production.

6. Deployment to production

Let's begin by defining deployment and production. Deployment is the process of moving code or an application from your local development environment to a remote environment, like a server or cloud platform. This enables automation, resource scaling, and makes your work accessible to others.

7. Deployment to production

Production represents the live environment where our pipeline runs for real users and business operations. When it comes to data and ML pipelines, production encompasses two main components: First, code - ensuring your code is robust, reproducible, and well-tested with unit tests and validation steps. Second, infrastructure - configuring the necessary network, security settings, and resource allocation to support reliable execution.

8. What could go wrong?

Once a pipeline is deployed to production, several types of issues can arise: Data integrity issues – such as upstream system errors, transformation problems, schema mismatches, or validation failures

9. What could go wrong?

Operational failures – caused by code bugs, missing dependencies, or incorrect logic

10. What could go wrong?

Infrastructure failures occur due to network outages, insufficient compute resources like CPU, memory, or GPUs, or other system limitations.

11. What could go wrong?

Model drift represents a gradual decline in model performance as the underlying data changes over time. These issues directly impact pipeline performance and translate into real business consequences - lost revenue, decreased customer satisfaction, or operational inefficiencies that compound into significant losses over time. That's why monitoring and observability are crucial. They provide the tools to track pipeline health, quickly spot problems, and maintain reliable performance.

12. Monitoring and observability

While closely related, these two concepts serve different purposes: Observability refers to systems that capture logs and metrics. When a failure occurs, these logs and metrics help us identify the root cause - like detective work gathering evidence at a crime scene.

13. Monitoring and observability

Monitoring analyzes those logs and metrics, alerting us when failures occur based on predefined logic - like a security system that sounds an alarm when it detects unusual activity.

14. Monitoring and observability

Think of it this way: observability is the foundation that collects the evidence, while monitoring is the intelligence layer that uses it to raise alerts. Infrastructure monitoring is typically handled by dedicated Site Reliability Engineering (SRE) or DevOps teams. They maintain the company's infrastructure using tools like Grafana and Prometheus, which fall outside our course scope. In the next lesson, we will dive into monitoring the data pipeline.

15. Let's practice!

Time to test your understanding!

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