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MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform

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 data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (GCP) - Deploying AI & ML Models in Production using Google Cloud Platform

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MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform

There is one session available:

56 already enrolled!
After a course session ends, it will be archivedOpens in a new tab.
Starts Jul 1

MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform

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 data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (GCP) - Deploying AI & ML Models in Production using Google Cloud Platform

MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform
Estimated 4 weeks
5–7 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Jul 1

About this course

Skip About this course

This is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).

Data Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1 (GCP): Deploying AI & ML Models in Production.

You will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.

Most importantly, by the end of this course, you will know...

  • What data engineers need to know to work effectively with data scientists
  • How to embed a predictive model in a pipeline that takes in data and outputs predictions automatically
  • How to monitor the model’s performance and follow best practices

At a glance

What you'll learn

Skip What you'll learn
  • What data engineers need to know in order to work effectively with data scientists

  • How to use a machine learning model to make predictions

  • How to embed that model in a pipeline that takes in data and outputs predictions automatically

  • How to measure the performance of the model and the pipeline, and how to log those metrics

  • How to follow best practices for “versioning” the model and the data

  • How to track and store model and data artifacts

  • Week 1: The Machine Learning Pipeline

    • AI Engineering Role

    • ML pipeline lifecycle

  • Week 2: The Model in the Pipeline

    • Case Study for the Course

    • Model Understanding

  • Week 3: Monitoring Model Performance

    • Logging and Metric Selection

    • Model and Data Versioning

  • Week 4: Training Artifacts and Model Store

About the instructors

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.

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