MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform

Monthly Subscription Starting at AED 99 + VAT

Level Awareness
Duration 38 hours
Course by Statistics.com
Total students 317 enrolled
Offered by

About

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline's functions and continuously optimize its performance, which is why we developed this course, MLOps2 (GCP): Data Pipeline Automation & Optimization using Gogle Cloud Platform. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.

What you will learn

You will learn how to set up automated monitoring of your data pipeline for prediction and get hands on experience with topics like data pipelines, drift and feedback loops, model stability, triggers & alarms, model security, responsible AI and much more.

But most importantly, by the end of this course, you will know…

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops
How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD)

Skills you learn

Syllabus

Week 1 – Drift and Feedback Loops

Module 1: Training Versus Inference Pipelines
Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

Module 3: Triggers & Alarms
Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

Module 5: CI/CD

Week 4 – Model Security and Responsible AI

Module 6: Responsible AI

Auto Summary

Discover how to overcome deployment challenges in data science with "MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform." This 38-hour course by edX focuses on automating and optimizing data pipelines on GCP. Ideal for IT and Computer Science professionals, it offers both Starter and Professional subscription options, catering to those seeking to enhance their awareness and skills in MLOps.