Smart Analytics, Machine Learning, and AI on GCP
Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
Understand use-cases for real-time streaming analytics.
Use Pub/Sub asynchronous messaging service to manage data events. Write streaming pipelines and run transformations where necessary.
Understand both sides of a streaming pipeline: production and consumption.
Interoperate Dataflow, BigQuery and Pub/Sub for real-time streaming and analysis.
Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction
Introduction to Analytics and AI
Prebuilt ML model APIs for Unstructured Data
Big Data Analytics with Notebooks
Week 2
Production ML Pipelines with Kubeflow
Custom Model building with SQL in BigQuery ML
Custom Model Building with AutoML
Summary
FAQ
Can I preview a course before enrolling?
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
What will I get when I enroll?
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
When will I receive my Course Certificate?
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
Why can’t I audit this course?
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
Reviews
Excellent course. Gets pretty advanced with developing ML pipelines with Kubernetes Engine, but otherwise very accessible.
Great course to have a complete overview of the GCP platform and components.
Content was fun and exciting but some exercises/graded labs inside this course are very unclear with the instructions and also took a long time to finish (model training).
Few important concepts like kubeflow should have been covered in a bit more detail.
