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Statistics.comX: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services

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 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services.

MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
4 weeks
5–6 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

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Starts Nov 18
Ends Dec 31
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MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services

About this course

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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 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services. 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.

At a glance

  • Language: English
  • Video Transcript: English
  • Associated programs:
  • Associated skills:Automation, Machine Learning, Amazon Data Pipeline, Amazon Web Services, Data Science, Data Pipeline, Forecasting, Addressing Ethical Concerns, Mathematical Optimization

What you'll learn

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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)

Syllabus

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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

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Machine Learning Operations with Amazon Web Services (MLOps with AWS) Professional Certificate Program

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Expert instruction
3 skill-building courses
Self-paced
Progress at your own speed
3 months
5 - 7 hours per week

Ways to take this course

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Certificate

Free

Price

$189 USD

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Limited

Expires on Dec 16

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